PSO_with_Objective_Funcations.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "nmPyxyntM84Y",
        "Mf4O9CkiM4VZ"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install optproblems"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJhUCeK_v3UG",
        "outputId": "e7cda478-3e2f-438a-ba6e-64a69ef9264f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: optproblems in /usr/local/lib/python3.7/dist-packages (1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from math import sin, sqrt\n",
        "from  optproblems import cec2005\n",
        "from  optproblems import Individual\n",
        "import statistics\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "QKaCpsdvtpL7"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Problem:\n",
        "  def __init__(self,f):\n",
        "    self.f = f\n",
        "  \n",
        "  def evaluate(self,sol):\n",
        "      func =  self.f(len(sol))\n",
        "      solution=Individual(sol)\n",
        "      func.evaluate(solution)\n",
        "      return solution.objective_values\n",
        "\n",
        "  def optimal(self,length):\n",
        "    func = self.f(length)\n",
        "    solutions = func.get_optimal_solutions()\n",
        "    for sol in solutions:\n",
        "      func.evaluate(sol) # return single solution\n",
        "      return sol.objective_values\n",
        "    return 0"
      ],
      "metadata": {
        "id": "MciINy9q4eMW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Particle:\n",
        "    \"\"\"A particle consists of two parts:\n",
        "          The particle’s location in space, ~x = hx1, x2, ...i. This is the equivalent, in evolutionary algorithms, of the individual’s genotype.\n",
        "          The particle’s velocity, ~v = hv1, v2, ...i. This is the speed and direction at which the particle is traveling each timestep\"\"\"\n",
        "    def __init__(self, bounds,w,c1,c2,num_variables):\n",
        "        self.particle_velocity = []                     \n",
        "        self.particle_position = []                     \n",
        "        self.best_position = [] # particle best position        \n",
        "        self.best_pos_fitness = float(\"inf\") # initialize best position fitness \n",
        "        self.particle_pos_fitness = float(\"inf\")\n",
        "        self.num_variables = num_variables                 \n",
        "        self.w=w\n",
        "        self.c1=c1\n",
        "        self.c2=c2\n",
        "\n",
        "       \n",
        "        for i in range(self.num_variables):\n",
        "        #define random initial position and velocity\n",
        "            self.particle_position.append(random.uniform(bounds[0],bounds[1]))\n",
        "            self.particle_velocity.append(random.uniform(-1, 1))\n",
        "    #Evalaute using Objective Funcations \n",
        "    def evaluate(self, objective_fun):\n",
        "        self.particle_pos_fitness = objective_fun.evaluate(\n",
        "            self.particle_position)\n",
        "        \n",
        "        if self.particle_pos_fitness < self.best_pos_fitness:\n",
        "          #update best position and fitness\n",
        "          self.best_position = self.particle_position\n",
        "          self.best_pos_fitness = self.particle_pos_fitness\n",
        "\n",
        "    def update_position(self, bounds):\n",
        "        for i in range(self.num_variables):\n",
        "            self.particle_position[i] = self.particle_position[i] + \\\n",
        "                self.particle_velocity[i]\n",
        "\n",
        "            # checking with the upper bounds\n",
        "            if self.particle_position[i] > bounds[1]:\n",
        "                self.particle_position[i] = bounds[1]\n",
        "            # checking with the lower bounds\n",
        "            if self.particle_position[i] < bounds[0]:\n",
        "                self.particle_position[i] = bounds[0]\n",
        "\n",
        "    # update particles by cheinging their positions and velocity depending on information of best position and global best position\n",
        "    \"\"\"Velocity update\n",
        "        At each iteration, velocity of a particle is changed according to four criteria\n",
        "        1-\t Own best position so far (cognitive component): a coefficient (𝛽) is used to\n",
        "             weight this component, usually called the cognitive weight\n",
        "        2-\tInformants’ best position (social component): a coefficient (𝛾) is used to\n",
        "            weight this component, called the social weight\n",
        "        3-\tGlobal best position (part of social component): weighted by a coefficient (𝛿)\n",
        "            called the global weight\n",
        "        4-\tAn inertia principle by which we consider the previous velocity, using an\n",
        "            inertia weight (𝛼)\"\"\"  \n",
        "    def update_velocity(self, global_best_pos, fittest_informant):\n",
        "        self.fittest_informant=fittest_informant\n",
        "        for i in range(self.num_variables):\n",
        "            r1 = random.random()\n",
        "            r2 = random.random()\n",
        "\n",
        "            cognitive_velocity = self.c1*r1 * \\\n",
        "                (self.best_position[i] -\n",
        "                 self.particle_position[i])\n",
        "            #update social velocity by informant fittest\n",
        "            social_velocity = self.c2*r2 * \\\n",
        "                (fittest_informant.particle_position[i] - self.particle_position[i])\n",
        "            globalVelocity=self.c2*r2 * \\\n",
        "                (global_best_pos[i] - self.particle_position[i])    \n",
        "            self.particle_velocity[i] = self.w*self.particle_velocity[i] + \\\n",
        "                cognitive_velocity + social_velocity+globalVelocity"
      ],
      "metadata": {
        "id": "iZiU5txEuOza"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate swarm and return the fittest particle based on previous position\n",
        "def get_current_best(swarm, objective_fun):\n",
        "    fitnesses = [assess_fitness(x.particle_position, objective_fun) for x in swarm]\n",
        "    best_value = min(fitnesses)\n",
        "    best_index = fitnesses.index(best_value)\n",
        "    return swarm[best_index]\n",
        "\n",
        "def assess_fitness(individual, objective_fun):\n",
        "    \"Determines the fitness of an individual using the given problem\"\n",
        "    return objective_fun.evaluate(individual)\n"
      ],
      "metadata": {
        "id": "JL9VbyX4TAfl"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class PSO():\n",
        "    def __init__(self, objective_fun, bounds, num_particles, iterations,num_informants,w,c1,c2,num_variables):\n",
        "        self.num_informants = num_informants #informants: set of particles with which a particle shares information\n",
        "        self.global_best_fitness = float(\"inf\")\n",
        "        self.global_best_pos = []\n",
        "        global_best_positions=[]\n",
        "        swarm_particle = []\n",
        "        self.history = []\n",
        "        for i in range(num_particles):# Particle size= number of particles\n",
        "            swarm_particle.append(Particle(bounds,w,c1,c2,num_variables))\n",
        "\n",
        "        for i in range(iterations):\n",
        "            for j in range(num_particles):\n",
        "                swarm_particle[j].evaluate(objective_fun)\n",
        "                \n",
        "                if swarm_particle[j].particle_pos_fitness < self.global_best_fitness:\n",
        "                  self.global_best_pos = list(swarm_particle[j].particle_position)\n",
        "                  self.global_best_fitness = float(swarm_particle[j].particle_pos_fitness)\n",
        "            self.history.append(self.global_best_fitness)\n",
        "\n",
        "            ## iterate to assign particle informants and update the position and velocity for particle with gbest and ibest\n",
        "            for j in range(num_particles):\n",
        "                #informants find fittest\n",
        "                informants = np.random.choice(swarm_particle, self.num_informants)\n",
        "                if swarm_particle[j] not in informants:\n",
        "                  np.append(informants, swarm_particle[j])\n",
        "                  #find the best in swarm by informants    \n",
        "                fittest_informant = get_current_best(informants, objective_fun)\n",
        "\n",
        "                swarm_particle[j].update_velocity(self.global_best_pos, fittest_informant)\n",
        "                swarm_particle[j].update_position(bounds)       \n",
        "        \n",
        "    def get_fittest_global_Pos(self):\n",
        "      return self.global_best_pos\n",
        "    def get_global_best_fitness(self):\n",
        "      return self.global_best_fitness"
      ],
      "metadata": {
        "id": "WlN7BchrK5ib"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation & Experiments"
      ],
      "metadata": {
        "id": "NAg7igwodKFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1 = Problem(cec2005.F1)\n",
        "f2 = Problem(cec2005.F2)\n",
        "f3 = Problem(cec2005.F3)\n",
        "f4 = Problem(cec2005.F4)\n",
        "f5 = Problem(cec2005.F5)\n",
        "f6 = Problem(cec2005.F6)\n",
        "f8 = Problem(cec2005.F8)"
      ],
      "metadata": {
        "id": "deEQRgR24h4N"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS, NUM_PARTICLES, ITERATIONS, NUM_INFORMANTS,W,C1,C2,runs=50):\n",
        "  ## Evaluation \n",
        "  evaluation = {\n",
        "      'accuracies': [],\n",
        "      'timedRuns': [],\n",
        "      'runs': [],\n",
        "  }\n",
        "  for run in range(runs):\n",
        "    print('RUN ',run+1)\n",
        "    start_time = datetime.now()\n",
        "    pso = PSO(OBJECTIVE_FUNC, BOUNDS, NUM_PARTICLES, ITERATIONS, NUM_INFORMANTS,W,C1,C2,VECTOR_LENGTH)\n",
        "    score,solution=pso.get_global_best_fitness(), pso.get_fittest_global_Pos()\n",
        "    delta_time = datetime.now() - start_time \n",
        "    timeTaken = delta_time.total_seconds() * 1000\n",
        "    accuracy = (score / OBJECTIVE_FUNC.optimal(VECTOR_LENGTH)) * 100\n",
        "\n",
        "    evaluation['timedRuns'].append(timeTaken)\n",
        "    evaluation['accuracies'].append(accuracy)\n",
        "    evaluation['runs'].append(pso.history)\n",
        "\n",
        "    print('Time taken->',timeTaken, 'ms')\n",
        "    print('Fittest individual->',solution)\n",
        "    print('Accuracy ->',accuracy,'%')\n",
        "    print('#####################################')\n",
        "  return evaluation"
      ],
      "metadata": {
        "id": "RQqBMVLjUwc8"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Experiments parameetrs \n",
        "experiment_runs = 25"
      ],
      "metadata": {
        "id": "Bd0RWaLuYY9G"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment 1"
      ],
      "metadata": {
        "id": "eQgf_I7lgwug"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "SF5b88c_M09o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment 1 for F1\n",
        "## Hyperparameters\n",
        "print('F1')\n",
        "OBJECTIVE_FUNC=f1\n",
        "VECTOR_LENGTH = 10\n",
        "BOUNDS= (-100,100)\n",
        "NUM_PARTICLES =50\n",
        "ITERATIONS=30\n",
        "NUM_INFORMANTS=2\n",
        "W = 0.1  # inertia constant\n",
        "C1 = 0.1   # cognative constant\n",
        "C2 = 1    # social constant\n",
        "exp_f1 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS,NUM_PARTICLES,ITERATIONS,NUM_INFORMANTS,W,C1,C2,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upc4bFj9gxpY",
        "outputId": "d51b48d4-146c-4ea2-f750-7f55a76cac3d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1\n",
            "RUN  1\n",
            "Time taken-> 372.697 ms\n",
            "Fittest individual-> [-39.263555463142694, 58.93300970037971, -46.37390952983018, -74.54914893044926, -16.820278702032688, -80.60297562912059, -10.64562703511144, 24.953445620865022, 89.84470564153906, 9.095177650414058]\n",
            "Accuracy -> 99.99472368658215 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 419.293 ms\n",
            "Fittest individual-> [-39.413145299803794, 58.837021197775854, -46.37087769895834, -74.59659556753054, -16.90577724314611, -81.45600773950173, -10.491499667941962, 24.992112153851956, 89.85333715972612, 9.11638013628568]\n",
            "Accuracy -> 99.80587552324143 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 525.7180000000001 ms\n",
            "Fittest individual-> [-38.77796180091883, 58.95691326095799, -46.24433679050134, -74.76741687560023, -16.999995207259904, -80.43291691182235, -10.566586119781372, 24.91749685045181, 89.86202277102373, 8.949664420183419]\n",
            "Accuracy -> 99.91318940871379 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 364.29200000000003 ms\n",
            "Fittest individual-> [-39.252240407994236, 58.8877260028271, -46.28674894219123, -74.60279704538208, -16.80768218906154, -80.54898805307343, -10.603205993891319, 24.984842564664387, 89.84151630782975, 9.17529519901317]\n",
            "Accuracy -> 99.99737791133441 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 384.685 ms\n",
            "Fittest individual-> [-39.3235539709519, 58.8857350042043, -46.35501784581814, -74.68554914233354, -16.82778978346201, -80.55772212994894, -10.63173852140862, 24.980452527904173, 89.83159368603772, 9.15483863308302]\n",
            "Accuracy -> 99.998442505894 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 384.14799999999997 ms\n",
            "Fittest individual-> [-39.17224717790305, 59.16798821694253, -46.18083269533447, -74.66710897574873, -16.77977932110628, -80.47610378926689, -10.616055117477316, 24.94558055107502, 89.91720371387234, 9.138604118924947]\n",
            "Accuracy -> 99.97229362276931 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 379.767 ms\n",
            "Fittest individual-> [-39.36160106745943, 58.703957285833916, -47.665400924920334, -74.73948092583987, -16.653801320816147, -81.09675800655394, -10.237540777504929, 25.13334087032306, 90.21759385472933, 8.411604786062474]\n",
            "Accuracy -> 99.34072041978766 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 538.3480000000001 ms\n",
            "Fittest individual-> [-39.2772567203989, 58.90900885529855, -46.40098375039419, -74.639459622094, -16.854871310745153, -80.69643429922222, -10.52373964474454, 25.031599294917566, 89.84768011687372, 9.180711204114461]\n",
            "Accuracy -> 99.98946456029427 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 357.966 ms\n",
            "Fittest individual-> [-39.187609803884314, 58.428264645822026, -46.53914569613349, -74.84964398484854, -16.766747861849886, -80.33791899840529, -10.541490950512602, 25.124068757088658, 89.68675163702908, 9.275258752677036]\n",
            "Accuracy -> 99.90132559594927 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 386.56 ms\n",
            "Fittest individual-> [-39.27203965988533, 58.93789576960412, -46.25866473613644, -74.55069329435945, -16.9803462918765, -80.5988070249743, -10.642826794687231, 24.99100909656369, 89.7475513289158, 9.166293236985483]\n",
            "Accuracy -> 99.98511225926 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 371.102 ms\n",
            "Fittest individual-> [-39.24191995835489, 58.564039794807194, -45.98293946270006, -74.71039516089805, -16.577800674097983, -80.23617673614005, -10.989235237244667, 25.060608333259317, 89.63743163308666, 8.795945325581384]\n",
            "Accuracy -> 99.84764479863917 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 384.618 ms\n",
            "Fittest individual-> [-39.33819952820706, 58.905943869291626, -46.33223319423376, -74.67550142044605, -16.851843229232227, -80.58422697089922, -10.602056946762291, 24.904828293217435, 89.81626534489915, 9.136371953937289]\n",
            "Accuracy -> 99.99754186895085 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 392.658 ms\n",
            "Fittest individual-> [-39.43092433591935, 58.93270430577719, -46.42705603801448, -74.48151367577502, -17.37044318494174, -80.5682099341903, -10.408584678195796, 24.909136524212236, 89.97815246314096, 8.67511656324304]\n",
            "Accuracy -> 99.86009864263329 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 385.04 ms\n",
            "Fittest individual-> [-39.31006950134996, 58.884125251643106, -46.318364630303854, -74.64750091402391, -16.788609715872784, -80.5382077344123, -10.63193014576821, 24.932529401572076, 89.83937931958569, 9.098733759194687]\n",
            "Accuracy -> 99.99923270897777 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 402.708 ms\n",
            "Fittest individual-> [-39.52920739483641, 59.075402857414524, -46.35135651786139, -74.87358531936344, -16.513604924996486, -80.70010621362296, -10.594242942184357, 24.564193273311602, 89.78544954824108, 8.899043569774738]\n",
            "Accuracy -> 99.90073842000194 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 379.72499999999997 ms\n",
            "Fittest individual-> [-39.337143336652396, 58.885810729590695, -46.322834727155914, -74.63808626135926, -16.831555715451223, -80.54919719789685, -10.561381804626125, 24.98613711538352, 99.99997314978852, 9.121964190964324]\n",
            "Accuracy -> 77.05310255668009 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 378.214 ms\n",
            "Fittest individual-> [-39.558426743297474, 59.039576348321894, -47.0936238981034, -74.86029584449416, -16.878350105615226, -81.35569147779167, -10.472679375787063, 25.31077273426602, 89.86270475756223, 8.90483939581912]\n",
            "Accuracy -> 99.65374860643512 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 534.303 ms\n",
            "Fittest individual-> [-39.20045816565245, 58.886047278627, -46.39422680091745, -74.54798993192291, -16.89161946015124, -80.42574951405012, -10.565240713785334, 24.92269458921605, 89.72514247606772, 9.202326293282868]\n",
            "Accuracy -> 99.98335004612609 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 711.061 ms\n",
            "Fittest individual-> [-39.31987911676344, 58.89725514959476, -46.28860280787339, -74.66683577355919, -16.831037502716352, -80.52888071552258, -10.60037017941718, 24.94760743678095, 89.81487143674312, 9.115013542753655]\n",
            "Accuracy -> 99.99916729644553 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 742.186 ms\n",
            "Fittest individual-> [-39.30821856105083, 58.89374225122625, -46.30933214639515, -74.70889605867706, -16.785167166282804, -80.54391501002247, -10.582886013979463, 24.95332742717251, 89.77032239345762, 9.088588602355037]\n",
            "Accuracy -> 99.99793849983142 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 788.906 ms\n",
            "Fittest individual-> [-39.33207195719989, 59.099613693183116, -46.49212635098445, -74.72488429237055, -16.87198826904805, -80.43126725831705, -10.58154361996863, 25.055123712766424, 90.29600193484207, 9.18647492346247]\n",
            "Accuracy -> 99.9300235473201 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 774.6469999999999 ms\n",
            "Fittest individual-> [-39.24282470346248, 58.744760721278496, -46.29109813582054, -74.64196621853837, -16.73084197114825, -80.50927016978318, -10.642805242182394, 25.02234209233714, 89.81278838580494, 9.042784989092674]\n",
            "Accuracy -> 99.98965965580967 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 708.711 ms\n",
            "Fittest individual-> [-39.302739625048396, 58.91469767288335, -46.311962742118354, -74.68795811468426, -16.807175544980876, -80.56849240086686, -10.571220108761608, 24.966741579252087, 99.9756669245001, 9.187058973127153]\n",
            "Accuracy -> 77.16161668497263 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 803.177 ms\n",
            "Fittest individual-> [-39.765246284639645, 59.053751449587224, -46.43144653952287, -74.4875430751368, -16.533873124483417, -80.62177689639965, -9.842143545671352, 25.11274173938465, 89.90591582255205, 8.971870672378971]\n",
            "Accuracy -> 99.78801916757547 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 687.075 ms\n",
            "Fittest individual-> [-39.3084378110318, 60.11039679109206, -46.077116074736615, -74.47028919040201, -16.608203837039643, -80.57149013826191, -10.660820660335771, 25.05332468074069, 99.99484382489251, 8.877160836590958]\n",
            "Accuracy -> 76.70760753422495 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "nmPyxyntM84Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment 1 for F4\n",
        "## Hyperparameters\n",
        "print('F4')\n",
        "OBJECTIVE_FUNC=f4\n",
        "VECTOR_LENGTH = 10\n",
        "BOUNDS= (-100,100)\n",
        "NUM_PARTICLES =150\n",
        "ITERATIONS=50\n",
        "NUM_INFORMANTS=10\n",
        "W = 0.1  # inertia constant\n",
        "C1 = 0.1   # cognative constant\n",
        "C2 = 1    # social constant\n",
        "exp_f4 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS,NUM_PARTICLES,ITERATIONS,NUM_INFORMANTS,W,C1,C2,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MboXc7Iyg-GP",
        "outputId": "579402bf-db7a-4c76-bf07-d1708c8ce091"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F4\n",
            "RUN  1\n",
            "Time taken-> 5128.829 ms\n",
            "Fittest individual-> [34.22857007774971, -80.83796333787643, -8.555037984774962, -95.96823555234947, 99.93507782020825, 37.44910763739126, -84.14030712032108, -30.012222651732714, 74.76837859449635, -38.01648321063693]\n",
            "Accuracy -> 62.37908886903774 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 11268.337000000001 ms\n",
            "Fittest individual-> [35.71397275728241, -82.26866836003919, -12.615996033963993, -81.35291558890623, 81.6168409340773, 47.09419792269931, -87.25382950260891, -26.01428531500083, 69.80946722654161, -36.24290381781624]\n",
            "Accuracy -> 94.04686044294172 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 9970.7 ms\n",
            "Fittest individual-> [43.15441456645774, -96.83970081437191, 1.1182118800819496, -94.73572086556936, 92.52177705047056, 41.43390231477702, -85.36860477343332, -30.798302112571882, 79.89934613029473, -42.089303906300024]\n",
            "Accuracy -> 58.00677097867803 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 4376.442 ms\n",
            "Fittest individual-> [35.49988967399265, -82.74691942403761, -10.668527848382855, -83.61082618050476, 83.23033736479468, 46.92425550144901, -89.31774369999991, -27.476245069590938, 76.10211457152973, -39.01617797160044]\n",
            "Accuracy -> 99.9931621130439 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 4319.3679999999995 ms\n",
            "Fittest individual-> [38.89025969476013, -89.30370365258366, -5.962673107941345, -85.4770020648554, 84.1152779471905, 45.70759679718892, -89.17167047493243, -26.553204329444743, 75.50960225481418, -38.70017117499321]\n",
            "Accuracy -> 94.59659041094184 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 4356.434 ms\n",
            "Fittest individual-> [36.2811025071489, -83.50440346570639, -11.493091984997815, -82.45482234460594, 81.54578173292353, 52.71939933059798, -96.97803335347037, -24.84468767190573, 76.38446327182211, -38.41681444478056]\n",
            "Accuracy -> 92.76611049259084 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 4464.000999999999 ms\n",
            "Fittest individual-> [21.459325380498257, -54.95177989425135, -30.096307083475903, -73.81966480420834, 76.73325702298433, 54.76937447864197, -99.97917312360585, -17.16850184202065, 69.34700876153522, -34.841404105921015]\n",
            "Accuracy -> -19.18031141145083 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 4430.905 ms\n",
            "Fittest individual-> [36.3632194754438, -84.02437461304788, -12.29031850918499, -79.49599562134088, 81.66366323841024, 46.458597192997736, -89.23777186499004, -39.749650055287496, 99.50888289574017, -49.47903067061566]\n",
            "Accuracy -> 37.00270505436117 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 4345.798000000001 ms\n",
            "Fittest individual-> [36.81185990883118, -87.82986847801912, -5.805268853318227, -85.37034212775542, 85.7946237463203, 43.98338250845382, -87.33552224842438, -26.736475614395065, 72.7931406336587, -36.76742996236]\n",
            "Accuracy -> 93.53083796886666 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 4284.707 ms\n",
            "Fittest individual-> [26.26284371212092, -64.11297848808152, -26.505043665170895, -60.663352692694005, 55.42509224067908, 46.492639565014, -76.58109659816988, -30.734066192059274, 95.78675880706746, -59.77604886819519]\n",
            "Accuracy -> -138.9064781195663 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 4364.074 ms\n",
            "Fittest individual-> [33.06029695484926, -80.8886196137382, -7.549745602262925, -85.89207099743544, 81.40252033857573, 49.73235069077825, -90.21987337504461, -28.212653186429197, 76.851691093335, -39.34792658891725]\n",
            "Accuracy -> 96.09993795037552 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 4338.8099999999995 ms\n",
            "Fittest individual-> [34.78481200435554, -83.7466788071809, -6.429251350296665, -87.12975501226458, 87.44771070292009, 41.945623222388335, -88.2406977679657, -25.78943639928075, 73.37188893281989, -36.86874590580062]\n",
            "Accuracy -> 93.38481858047855 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 6651.903 ms\n",
            "Fittest individual-> [33.22713456743806, -78.71625174998753, -13.342417794010723, -79.75966403810398, 76.59498106367559, 57.36848781438685, -98.42830006998278, -27.989286921675102, 80.13449481642304, -40.668698804712655]\n",
            "Accuracy -> 79.67282114096346 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 4310.514999999999 ms\n",
            "Fittest individual-> [33.93862835610586, -62.04166005515013, -59.722815077124956, -44.16170812895922, 86.32963849074277, 21.516051519668842, -66.6126191517981, -40.1656493737553, 59.96476233887514, -2.882411519032689]\n",
            "Accuracy -> -443.49406273689755 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 4366.345 ms\n",
            "Fittest individual-> [35.22784562635402, -76.94965801440684, -21.494564934963492, -76.40328199328293, 81.81103502239702, 44.86498711455117, -86.5757278805976, -29.548568508981045, 77.07795545082487, -39.239138892545036]\n",
            "Accuracy -> 84.81563124395565 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 4186.8550000000005 ms\n",
            "Fittest individual-> [33.55402903022083, -79.79457906852672, -9.754754186365547, -86.73560362548912, 82.86333306293274, 48.671007592627284, -89.15950214826813, -28.23030099499382, 76.61613089812482, -38.87035462735324]\n",
            "Accuracy -> 97.04281999470375 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 4307.157 ms\n",
            "Fittest individual-> [34.45528306438592, -78.87812688576017, -14.728410836221858, -81.89545261544846, 84.39722276294911, 46.34984768081956, -93.4580657748716, -21.214035361160374, 70.37238413671017, -36.397558841795124]\n",
            "Accuracy -> 90.85001369173041 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 4433.242 ms\n",
            "Fittest individual-> [35.63396029411296, -82.86747435298714, -10.851132726400348, -83.25782633119013, 83.14330406397947, 46.72790512810258, -89.21364440306337, -27.556097941851245, 76.30070500285349, -39.160571969723684]\n",
            "Accuracy -> 99.97277240755358 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 4395.975 ms\n",
            "Fittest individual-> [36.44647768817777, -82.5042862294838, -16.74651775485734, -73.62864066780006, 74.4078716824153, 54.13963385183589, -93.40660559687582, -29.634912353341363, 79.64294607024033, -40.42345045526004]\n",
            "Accuracy -> 80.89745984588066 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 4385.102 ms\n",
            "Fittest individual-> [26.072574836373843, -62.20837597243947, -22.515115096495617, -81.80184459273208, 83.32844522625797, 35.352530522529875, -74.96295961427911, -35.10353866785266, 80.58655280947659, -37.84120402041735]\n",
            "Accuracy -> 19.257405983327732 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 4364.187 ms\n",
            "Fittest individual-> [38.9365959294077, -97.54480608544128, 10.284664806404056, -99.97996677834816, 85.97254643776752, 58.545084141892154, -99.99999979811211, -20.53893745013353, 69.01989382665285, -35.71491207880819]\n",
            "Accuracy -> 14.48174456998077 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 4517.8330000000005 ms\n",
            "Fittest individual-> [31.429527118816544, -78.8469755824437, -10.400615331649112, -83.76994136815146, 83.16007209455783, 48.65794312332561, -92.11000738795654, -27.172554749155417, 78.78784260287476, -41.88927874495861]\n",
            "Accuracy -> 94.14330153442066 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 4377.968 ms\n",
            "Fittest individual-> [34.80829500571328, -80.11226307956338, -6.736247570458424, -97.02667793597115, 92.2731261731025, 44.20895312469308, -87.6005577076614, -27.69503128881207, 75.17302405242084, -37.94779383589356]\n",
            "Accuracy -> 77.4414685893227 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 4975.8240000000005 ms\n",
            "Fittest individual-> [35.237882800854464, -82.50586118274305, -11.005863160855888, -83.11590882904007, 83.72675291555049, 45.628460806750354, -87.3956009645776, -29.103630081798638, 76.55165943858422, -39.40344284896002]\n",
            "Accuracy -> 99.2732682642725 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 5397.849 ms\n",
            "Fittest individual-> [45.44625164138235, -99.99762328970294, -8.272043827244575, -77.5066889596562, 82.59554681100349, 47.6134141932333, -92.7664378551512, -24.126815710020978, 75.02054956978816, -38.46432531808833]\n",
            "Accuracy -> 59.39587769548616 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "Mf4O9CkiM4VZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment 1 for F8\n",
        "## Hyperparameters\n",
        "print('F8')\n",
        "OBJECTIVE_FUNC=f8\n",
        "VECTOR_LENGTH = 10\n",
        "BOUNDS= (-32,32)\n",
        "NUM_PARTICLES =10\n",
        "ITERATIONS=10\n",
        "NUM_INFORMANTS=2\n",
        "W = 0.2  # inertia constant\n",
        "C1 = 0.1   # cognative constant\n",
        "C2 = 0.2    # social constant\n",
        "exp_f8 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS,NUM_PARTICLES,ITERATIONS,NUM_INFORMANTS,W,C1,C2,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgYKGaCHhHvI",
        "outputId": "9aa1c5cb-55f4-4852-8d5a-62c45b9956cd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F8\n",
            "RUN  1\n",
            "Time taken-> 39.752 ms\n",
            "Fittest individual-> [0.7414636932160037, -0.16513397465615354, 0.5779066921732352, -22.85475705831077, 6.7450897913251175, 11.59921678286445, -16.165582829975275, 19.787691581732258, -7.448105072088645, 13.742709662895777]\n",
            "Accuracy -> 84.9748291842952 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 35.95099999999999 ms\n",
            "Fittest individual-> [5.015749657261434, -5.678310695713338, -5.228317222727565, 10.199963569970741, -18.2496364104499, -18.434008524433374, -22.066779923143454, -3.4922592685934393, -0.28434453937054577, -11.145340545479375]\n",
            "Accuracy -> 85.17985250761737 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 35.11 ms\n",
            "Fittest individual-> [-0.11560859060899897, 8.3462775666442, -7.81257665024719, 6.524441849431142, 4.524234476708953, -14.505939680311233, 1.622603916765966, 3.1952476718057277, -0.5917545980710062, 6.560974169056458]\n",
            "Accuracy -> 84.98467303031228 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 45.078 ms\n",
            "Fittest individual-> [3.221874706895906, -6.569727974551868, 2.690485996470907, -1.9112782378548474, 25.085032069780663, 10.485987559112733, 2.9848923191806205, -3.1544699713553555, 4.079256598452879, -5.7391321146463605]\n",
            "Accuracy -> 84.91600375759144 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 32.872 ms\n",
            "Fittest individual-> [-13.652533137353535, 9.283641971013221, -0.044710448518388346, -6.970772835005451, 11.332723763168795, -0.31492567165561014, 27.992986867033416, -0.9133727795605293, -0.6839015184147159, 24.40958232914801]\n",
            "Accuracy -> 84.91874900367337 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 37.995000000000005 ms\n",
            "Fittest individual-> [-2.1124559693313674, -14.079995839767637, -13.644611442850445, 11.338696752585477, 20.361262518219426, -20.09992980593167, 0.6889456599725721, 21.079848830155147, -7.205201565474287, 15.652723614466979]\n",
            "Accuracy -> 85.15620042708379 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 34.618 ms\n",
            "Fittest individual-> [13.859148243528217, -2.7775274251937487, -24.453698387003058, 2.6869987654902285, 3.0953616573521323, 11.87991195065337, 2.992077535574934, 25.65806796128627, -24.07115850182157, 26.497027718861908]\n",
            "Accuracy -> 84.93059577815289 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 33.704 ms\n",
            "Fittest individual-> [2.5813253748932956, 0.6223608232469037, -2.603362636512344, 3.991869822018144, -2.4426830743211583, -2.327416055772071, 14.561139362625076, -11.085925689549809, 8.457911945502675, 2.4441387514974293]\n",
            "Accuracy -> 85.17607298058059 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 31.291 ms\n",
            "Fittest individual-> [10.361164245392844, -7.321970256684411, -4.58143723174906, 8.784117767906883, 9.751326996137994, 7.55076155289696, -8.60869453338408, 10.570387312814734, -9.323806079697967, 13.169995667689587]\n",
            "Accuracy -> 84.84422377467303 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 38.791 ms\n",
            "Fittest individual-> [-1.0386546424183716, 16.5707305350245, 3.6030362253212513, -4.871672131585116, 1.6774435510917294, 0.11711756087848252, -13.397048427868745, 20.321849019717252, 5.749922446673772, 1.0073360860490679]\n",
            "Accuracy -> 84.9258381270615 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 39.275999999999996 ms\n",
            "Fittest individual-> [4.168603297334375, -8.965217925620188, 1.0972476432481857, 4.6922050582537285, 8.589002254779537, 2.4730834321317263, 21.80703904203261, 3.395959247988428, -19.23657821616965, 10.03303695385414]\n",
            "Accuracy -> 85.19250046885993 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 33.477 ms\n",
            "Fittest individual-> [19.689574528894074, 12.762160832592606, 21.57495025407201, 22.45682974955272, -27.680966943635795, -11.34346535574922, -14.108606338341144, -21.263023707111333, -12.775392865994695, 20.990532325269374]\n",
            "Accuracy -> 84.98026273495664 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 45.531 ms\n",
            "Fittest individual-> [8.789365580624276, -14.992257264691263, -22.795845657635446, 11.204048044258798, 13.917397229206276, -2.5961527497503276, 9.96609118765647, 13.008509637547807, -12.770195973927994, -26.193166415764516]\n",
            "Accuracy -> 85.09359818678992 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 34.33 ms\n",
            "Fittest individual-> [2.131015700645132, -13.919150639574697, 0.6711545178141561, 0.28716104238939644, -8.331302758102693, -5.734857832117925, 11.30420006024572, -11.996593796193205, 2.3502197430715412, -2.574514651833634]\n",
            "Accuracy -> 84.98242029782288 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 36.447 ms\n",
            "Fittest individual-> [23.789994905586205, -1.5720348115938236, -20.884529458191416, 21.09362327217648, -15.339164810639673, -17.039973278891885, 29.86994805579262, -16.392565954896874, -23.620816686904334, -17.303800695881314]\n",
            "Accuracy -> 84.97938099854431 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 37.14 ms\n",
            "Fittest individual-> [11.316471446727737, -27.334464346130357, 11.689057833625776, -7.314837418859648, -9.897283622458174, -9.787506086247115, 4.800496372310583, 3.2007605047092365, -1.1235151177280098, -11.356091709040268]\n",
            "Accuracy -> 84.89736348376245 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 65.666 ms\n",
            "Fittest individual-> [-6.979423272196563, 4.660019831424372, 4.998496524793265, -9.696360803324353, 6.5572996245750845, -11.816957523752382, -5.954223530817944, -7.546359268308936, -2.9073427523720983, 19.30674846819342]\n",
            "Accuracy -> 85.01156078182454 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 36.720000000000006 ms\n",
            "Fittest individual-> [1.8831929268629908, -0.09211239481646268, -9.804725541859717, 7.841711634560838, 3.8567301978138673, -6.513077757587203, 14.40091386367246, 3.263462322936496, 4.200156768744004, 1.9457589673949083]\n",
            "Accuracy -> 85.14346100804264 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 36.278 ms\n",
            "Fittest individual-> [6.253795716153489, -9.39959075192132, 0.17381370676097702, -3.910100163201598, -13.791164013279674, 10.801375526681257, -7.7621033288842165, -0.4634239428899287, -5.914016415909555, 12.488916665100346]\n",
            "Accuracy -> 84.83609177296375 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 34.687000000000005 ms\n",
            "Fittest individual-> [-8.970910826175377, -17.47869577905951, 18.003496356696377, -4.056298817744001, -18.308004889481616, 4.78387931084071, -20.086569677543874, 4.994185591928122, 9.647192213570444, -9.213995306607352]\n",
            "Accuracy -> 84.87028838647404 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 35.817 ms\n",
            "Fittest individual-> [7.528113919550649, 0.26776604825678874, 13.059427858078642, 11.676947876041293, -12.365089031787964, -8.42851620137091, -1.3816901851154024, -0.7333340274898976, 3.5057299353012006, 8.8835079803533]\n",
            "Accuracy -> 85.11844135471345 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 32.904999999999994 ms\n",
            "Fittest individual-> [-24.69661391287703, -8.663973497748568, -6.495543993685086, -6.639537147575538, 11.673799366653343, 23.99751869490238, 3.425141169646473, 11.653194964801772, 10.401577430280158, -1.1330435019483596]\n",
            "Accuracy -> 85.03142530964439 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 36.952 ms\n",
            "Fittest individual-> [4.847996916250706, -0.48178677067867703, -6.493073649804708, 10.078833536849627, -6.947852351890743, 13.78358384285739, 6.162755248655165, -0.8830395538676536, -12.7959017626211, -1.9201457774965491]\n",
            "Accuracy -> 85.1620849272436 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 34.745 ms\n",
            "Fittest individual-> [-2.8525067877177492, 7.914539703990458, -15.387867529403081, 5.924937917932481, -1.6416628546082817, -4.50430696196012, -5.50634304169089, 19.817668532806614, -10.260104505732155, -12.889195553910742]\n",
            "Accuracy -> 84.8710118014231 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 28.792 ms\n",
            "Fittest individual-> [14.869556239145354, -26.656236703736973, -1.6572272471998701, -6.055427596909842, -5.996752685163009, 8.69683691780157, -20.125750440548593, -1.6795731561665228, 11.228816394796855, -27.33312025496384]\n",
            "Accuracy -> 84.97524857412981 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis"
      ],
      "metadata": {
        "id": "2ocX8ZsRM_5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Compare F1,F4,F8 Accuracy\n",
        "x_axis = [i for i in range(experiment_runs)]\n",
        "plt.plot(x_axis, exp_f1['accuracies'])\n",
        "plt.plot(x_axis, exp_f4['accuracies'])\n",
        "plt.plot(x_axis, exp_f8['accuracies'])\n",
        "\n",
        "plt.title('Accuracy vs Runs')\n",
        "plt.xlabel('Runs')\n",
        "plt.ylabel('Accuracy(%)')\n",
        "plt.legend(['F1', 'F4','F8'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cbXT4SdwkBJg",
        "outputId": "630085e5-e4b4-4598-d48a-344baa12edf4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "_NohGBX2NEBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F1 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f1['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f1['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "UKuF4DYjjg9m",
        "outputId": "9e30444a-80d3-44c7-adce-3e79e900850e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "ifV_SwN-NFnB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F4 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f4['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f4['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "YMqCJn3sj7lT",
        "outputId": "14de9a79-bd68-4273-e535-5bbc4a136e27"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "jQnYZxAWNG4h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F8 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f8['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f8['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "oM1kFLUGkI6x",
        "outputId": "b2e4cc65-3b1f-49c5-96bd-a2a85a345748"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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i435M0sJ0WyJpl6S90n2nSVopaZWky0f+XdpgzWhPiglWbPDaOmbVLqsrnSXA2cCCPu3bgCuBywobJTUA1wCzIuJIkuWoL+k7aER8PiJmRsRM4Arg3oh4VlI98CXgdCAPzJGUH+b3ZHvo5elwXMFmVu0ySToRsTwiVhZp3xoR95Ekn0JKt2ZJAnLA+gFOMwf4bvr6WGBVRKyOiB3A94AzXst7sOEzpWUUk5qbPB2OWQ2oiGc6EdENXAQsJkk2eeDG/vpLGgucBvwobZoKPFHQZW3a1t/xF0jqlNTZ1dX1GqO3gby0to6LCcyq3oglHUl3pM9V+m5DvsKQ1EiSdI4G2klur12xm0PeBfwyIp7dk9gj4vqI6IiIjtbW1j0ZwoYo357jd09tZsfOnqxDMbMR1DBSA0fEycM43Mx0zEcAJN0E7K4Y4L28fGsNYB3wuoKf903brEzk23J07wpWbdzy0jMeM6s+FXF7jSRB5CX1XnacAiwv1lHSeOBE4McFzQ8AB0vaX1ITSVK6ZQTjtSHqrWDzLTaz6pZVyfRZktYCbwJulXRbwb41wBeB8yStlZSPiPXAJ4EFkhaRXPl8Ju1/oaQLC4Y/C7g9Irb2NkTETpJqt9tIktVNEbF0RN+kDcn+k5sZ01jvCjazKidPKb97HR0d0dnZmXUYNeGsL/+Spvo6vv+Xb8o6FDN7DSQ9GBEdxfZVyu01qwH5tqSCzf8QMqteTjpWNvLtOTZv28na517MOhQzGyFOOlY2eosJlvpLomZVy0nHysahe7dQJ1jmYgKzquWkY2VjTFM9B7SOc9m0WRVz0rGyMqM95znYzKqYk46VlXxbjvXPb+O5rTuyDsXMRoCTjpWV3ilwfIvNrDo56VhZybelSce32MyqkpOOlZVJ40axT260p8Mxq1JOOlZ2ZnhtHbOq5aRjZSffnuORrq1s696VdShmNsycdKzs5Nty7OoJVj65OetQzGyYOelY2fHaOmbVa8hJR9JESUeORDBmAPtOHEPLqAYXE5hVoUElHUn3SMpJ2gt4CPiapC+ObGhWq+rqxOFtnpnArBoN9kpnfERsAs4G/iMijgNO3tOTSjpH0lJJPZI6CtonSbpb0hZJ1/U5Zo6kxZIWSZovaXKRcT8maWG6LZG0K02USFqTHr9QkldlK3P59hwrntzMrh6vrWNWTQabdBoktQF/DPz3MJx3CUkCW9CnfRtwJXBZYaOkBuAaYFZEHAksIll++hUi4vMRMTMiZgJXAPdGxLMFXWal+4uuaGflI9+e44Udu1jzzNaBO5tZxRhs0vkUcBuwKiIekHQA8PCenjQilkfEyiLtWyPiPpLkU0jp1ixJQA5YP8Bp5gDf3dMYLVsz2j0zgVk1GlTSiYgfRMSREfGh9OfVEfGekQ3tFefvBi4CFpMkmzxwY3/9JY0FTgN+VDgMcLukByVdMILh2jA4eEoLjfVyBZtZlRlsIcHn0kKCRkl3SuqS9KcDHHNH+lyl73bGUIOU1EiSdI4G2klur12xm0PeBfyyz6214yPiGOB04GJJJ+zmfBdI6pTU2dXVNdRwbRg0NdRx0JQWryJqVmUGe3ttdlpI8E5gDXAQ8LHdHRARJ0fEEUW2H+9BnDPTMR+JiABuAt68m/7vpc+ttYhYl/65EZgHHLub2K+PiI6I6Ghtbd2DcG04eG0ds+oz6EKC9M93AD+IiFJ/gWIdkJfUmwFOAZYX6yhpPHAi8OOCtmZJLb2vgdkkxQxWxvJtOZ7esp2Nm/s+4jOzStUwcBcA/lvSCuBF4KL0l/8e/yaQdBZwLdAK3CppYUScmu5bQ1Io0CTpTJKrrGWSPgkskNQNPAacl/a/ECAi5qbDnwXcHhGFZU97A/OSGgQagO9ExPw9jd9Ko3dtnaXrNzHl0NEZR2Nmw0HJ3apBdEy+7/J8ROxKrxZaIuLJEY2uDHR0dERnp7/Wk4VN27o58hO387FTD+XiWQdlHY6ZDZKkB/v7aspgCwnGAh8CvpI2tQP+rouNqNzoRl631xg/1zGrIoN9pvMNYAcvP7xfB/zTiERkViDf5rV1zKrJYJPOgRHxOaAbICJeIPmyptmImtE+njXPbGXL9p1Zh2Jmw2CwSWeHpDEkX7BE0oHA9hGLyiyVb8sRASt8tWNWFQabdD4OzAdeJ+nbwJ3A345YVGapGVPT6XCcdMyqwqBKpiPi55IeAt5Iclvt0oh4ekQjMwP2yY1m4thGFxOYVYnBfk8HYDTwXHpMXhIR0XeWaLNhJYl8e87T4ZhViUElHUn/CvwJsBToSZuDVy9NYDbsZrSP55u/WkP3rh4a673CulklG+yVzpnAoRHh4gEruXxbjh07e1jdtZVD92nJOhwzew0G+8/G1UDjSAZi1p+Xp8Mp9ZR/ZjbcBnul8wKwUNKdFJRKR8RHRiQqswIHTG5mVEMdy9Zv4uxjso7GzF6LwSadW9KtkBevt5JoqK/jsH28to5ZNRhs0pkQEdcUNki6dATiMSsq357jp4ufJCJIZws3swo02Gc6HyjSdt4wxmG2W/n28Tz/Yjfrn/faOmaVbLdXOpLmAO8D9pdUeHutBXi2+FFmwy/flhYTrHueqRPGZByNme2pgW6v/QrYAEwGripo3wwsGqmgzPo6vK0FKZkOZ/aMfbIOx8z20G6TTkQ8RrJK55tKE45ZcWObGth/crOnwzGrcLt9piPpvvTPzZI2FWybJb2mv/2SzpG0VFKPpI6C9kmS7pa0RdJ1fY6ZI2mxpEWS5kuaXGTc8ZJ+Ium36fjnF+z7gKSH063YcyorY/k2T4djVukGKiQ4FyAiWiIiV7C1RETuNZ57CXA2r55KZxtwJXBZYaOkBuAaYFZEHElye++SIuNeDCyLiKOAk4CrJDWly21/HDgOOBb4uKSJr/E9WAnNaB/Put+/yPMvdGcdipntoYGSzrzeF5J+NJwnjojlEbGySPvWiLiPJPkUUro1K6mZzQHriw0NtKR9xpEUPOwETgV+HhHPRsRzwM+B04btDdmI652ZwMscmFWugZJO4RciDhjJQAYSEd3ARcBikmSTB24s0vU64PC0z2KSZRh6gKnAEwX91qZtryLpAkmdkjq7urqG703Ya/JSBZunwzGrWAMlnejn9aBIukPSkiLbGXswViNJ0jkaaCe5vXZFka6nAgvTPjOB6yQN6VZgRFwfER0R0dHa2jrUUG2EtLaMYkrLKF/pmFWwgUqmj0oLBgSMKSgeEBADPdeJiJOHIcZeM9MxHwGQdBNweZF+5wOfjYgAVkl6FDgMWEfyjKfXvsA9wxiflUC+PecKNrMKttsrnYioLygcaBjmQoKhWkeyeFzvpccpwPIi/R4H3gYgaW/gUJJZsm8DZkuamBYQzE7brILMaM+xauMWtnXvyjoUM9sDQ1k5dFhJOgu4FmgFbpW0MCJOTfetISkUaJJ0JjA7IpZJ+iSwQFI3yfeHzkv7XwgQEXOBTwPflLSY5Irs73qX1pb0aeCBNIRPRYRnVagw+bbx7OwJVm3cwhFTx2cdjpkNUWZJJyLmUVAd12ff9H7a5wJz+2nvfb2e5Cqm2PFfB76+B+FamShcW8dJx6zyeO1fqyjT9hpLc1O9n+uYVSgnHasodXXi8LacK9jMKpSTjlWc3gq2nh6vI2hWaZx0rOLMaM+xdccuHn/2haxDMbMhctKxipNvSwoIfIvNrPI46VjFOXjvcdTXydPhmFUgJx2rOKMb6zl4yjhXsJlVICcdq0heW8esMjnpWEXKt+fYuHk7XZu3Zx2KmQ2Bk45VpN6ZCZa7mMCsojjpWEV6eW0dJx2zSuKkYxVpwtgmpk4Y47JpswrjpGMVK5mZwGXTZpXESccqVr4tx+qnt/LCjp1Zh2Jmg+SkYxVrRnuOCFjx5OasQzGzQXLSsYrVW8HmL4maVY5Mko6kcyQtldQjqaOgfZKkuyVtkXRdn2PmSFosaZGk+ZImFxl3vKSfSPptOv75Bft2SVqYbreM7Du0Upg6YQzjxzS6gs2sgmR1pbMEOBtY0Kd9G3AlcFlho6QG4BpgVkQcCSwCLiky7sXAsog4CjgJuEpSU7rvxYiYmW7vHrZ3YpmRRN5r65hVlEySTkQsj4iVRdq3RsR9JMmnkNKtWZKAHLC+2NBAS9pnHPAs4KfMVSzfnmPFhk3s3NWTdShmNggV8UwnIrqBi4DFJMkmD9xYpOt1wOFpn8XApRHR+9totKROSf8r6czdnU/SBWnfzq6urmF7Hzb8ZrTn2L6zh0ef3pp1KGY2CCOWdCTdIWlJke2MPRirkSTpHA20k9xeu6JI11OBhWmfmcB1knLpvmkR0QG8D7ha0oH9nS8iro+IjojoaG1tHWq4VkIvFRP4FptZRRixpBMRJ0fEEUW2H+/BcDPTMR+JiABuAt5cpN/5wM2RWAU8ChyWHrsu/XM1cA9JArMKd2DrOJoa6lxMYFYhKuL2GrAOyEvqvew4BVhepN/jwNsAJO0NHAqsljRR0qi0fTLwFmDZiEdtI66xvo5D925x2bRZhWjI4qSSzgKuBVqBWyUtjIhT031rSAoFmtJnL7MjYpmkTwILJHUDjwHnpf0vBIiIucCngW9KWkxSePB3EfG0pDcDX5XUQ5JoPxsRTjpVIt+W4+fLnyIiSGpIzKxcZZJ0ImIeMK+ffdP7aZ8LzO2nvff1emB2kT6/Av5gD8O1Mpdvz/H9zid4ctM22saPyTocM9uNSrm9ZtavGZ6ZwKxiOOlYxTusLYfktXXMKoGTjlW8caMamD6p2Vc6ZhXASceqgqfDMasMTjpWFfLtOR5/9gU2bevOOhQz2w0nHasKvTMTLPctNrOy5qRjVWFGm6fDMasETjpWFVpbRjF5XJMr2MzKnJOOVQVJ5NvHu4LNrMw56VjVyLfleHjjZnbs9No6ZuXKSceqRr49R/eu4OGNm7MOxcz64aRjVcPT4ZiVPycdqxrTJzUzprHexQRmZcxJx6pGfZ04vK3FZdNmZcxJx6pKvj3H8vWbSBaYNbNy46RjVSXfNp7N23fyxLMvZh2KmRWRSdKRdI6kpZJ6JHUUtE+SdLekLZKu63PMHEmLJS2SND9ddrrvuBMlzUv73C/piIJ9p0laKWmVpMtH9h1aVl4qJtjwfMaRmFkxWV3pLAHOBhb0ad8GXAlcVtgoqQG4BpgVEUcCi4BLioz798DCtM/702OQVA98CTgdyANzJOWH7d1Y2Th0nxbq5Ao2s3KVSdKJiOURsbJI+9aIuI8k+RRSujVLEpAD1hcZOg/clY61ApguaW/gWGBVRKyOiB3A94Azhu0NWdkY3VjPga3jXMFmVqYq4plORHQDFwGLSZJNHrixSNffklxBIelYYBqwLzAVeKKg39q0rShJF0jqlNTZ1dU1LO/BSmdGu9fWMStXI5Z0JN0haUmRbchXGJIaSZLO0UA7ye21K4p0/SwwQdJC4MPAb4BdQz1fRFwfER0R0dHa2jrUwy1j+fYcG57fxrNbd2Qdipn10TBSA0fEycM43Mx0zEcAJN0EvKoYICI2AeenfQQ8CqwGxgCvK+i6L7BuGOOzMpJvGw8kz3WOP/hV9SZmlqGKuL1GkiDyknovO04BlvftJGmCpKb0x78AFqSJ6AHgYEn7p/vfC9xSgrgtA3lXsJmVrRG70tkdSWcB1wKtwK2SFkbEqem+NSSFAk2SzgRmR8QySZ8EFkjqBh4Dzkv7XwgQEXOBw4F/lxTAUuCD6b6dki4BbgPqga9HxNJSvV8rrb2am2gbP9rFBGZlKJOkExHzgHn97JveT/tcYG4/7b2v/wc4pJ/jfwr8dA/CtQo0oz3nsmmzMlQpt9fMhiTfluORri1s6x5yHYmZjSAnHatK+fYcPQErnvTaOmblxEnHqtKM9pcr2MysfDjpWFXad+IYWkY1uILNrMw46VhVksTh7TlXsJmVGScdq1oz2nOs2LCZXT1eW8esXDjpWNXKt+V4sXsXa57ZmnUoZpZy0rGq1VtM4FtsZuXDSceq1kFTxtFYL1ewmZURJx2rWk0NdRw8pYWl613BZlYunHSsqvVOhxPhYgKzcuCkY1Ut357jma076Nq8PetQzAwnHaty+bZkmQMXE5iVBycdq2qHv7S2jpOOWTlw0gY4/uMAAAnJSURBVLGqlhvdyH57jXUFm1mZcNKxqpdvy7mCzaxMZJJ0JJ0jaamkHkkdBe2TJN0taYuk6/ocM0fSYkmLJM2XNLnIuBMlzUv73C/piIJ9a9LjF0rqHNl3aOVkRnuONc+8wJbtO7MOxazmZXWlswQ4G1jQp30bcCVwWWGjpAbgGmBWRBwJLAIuKTLu3wML0z7vT48pNCsiZkZEx6sPtWqVT5/rrPBzHbPMZZJ0ImJ5RKws0r41Iu4jST6FlG7NkgTkgPVFhs4Dd6VjrQCmS9p7WIO3iuPpcMzKR0U804mIbuAiYDFJsskDNxbp+luSKygkHQtMA/btHQa4XdKDki7Y3fkkXSCpU1JnV1fXML0Ly8reuVHs1dzkYgKzMjBiSUfSHZKWFNnO2IOxGkmSztFAO8nttSuKdP0sMEHSQuDDwG+AXem+4yPiGOB04GJJJ/R3voi4PiI6IqKjtbV1qOFamZGUFBN4QTezzDWM1MARcfIwDjczHfMRAEk3AZcXOecm4Py0j4BHgdXpvnXpnxslzQOO5dXPlKxKzWjP8Y1frqF7Vw+N9RVxgW9WlSrlb986IC+p97LjFGB5306SJkhqSn/8C2BBRGyS1CypJe3TDMwmKWawGpFvz7FjVw+PdG3JOhSzmjZiVzq7I+ks4FqgFbhV0sKIODXdt4akUKBJ0pnA7IhYJumTwAJJ3cBjwHlp/wsBImIucDjw75ICWAp8MD3l3sC85OKHBuA7ETG/FO/VysNL0+Gs28Rh++QyjsasdmWSdCJiHjCvn33T+2mfC8ztp7339f8AhxTpsxo4ag/DtSpwQOs4RjfWsWzDJt6TdTBmNaxSbq+ZvSb1deLQfXKuYDPLWCZXOmZZyLfl+OGDT3DKF+/NOhSzsjdxbBM3XfimYR/XScdqxrnH7cembd1e0M1sEHKjG0dkXCcdqxlHTB3Pl953TNZhmNU0P9MxM7OScdIxM7OScdIxM7OScdIxM7OScdIxM7OScdIxM7OScdIxM7OScdIxM7OSkb+dvXuSukhmtd4Tk4GnhzGcSubP4pX8ebySP4+XVcNnMS0iiq6A6aQzgiR1RkRH1nGUA38Wr+TP45X8ebys2j8L314zM7OScdIxM7OScdIZWddnHUAZ8WfxSv48Xsmfx8uq+rPwMx0zMysZX+mYmVnJOOmYmVnJOOmMAEmnSVopaZWky7OOJ0uSXifpbknLJC2VdGnWMWVNUr2k30j676xjyZqkCZJ+KGmFpOWShn995Aoi6a/SvydLJH1X0uisYxpuTjrDTFI98CXgdCAPzJGUzzaqTO0E/iYi8sAbgYtr/PMAuBRYnnUQZeIaYH5EHAYcRQ1/LpKmAh8BOiLiCKAeeG+2UQ0/J53hdyywKiJWR8QO4HvAGRnHlJmI2BARD6WvN5P8UpmabVTZkbQv8A7ghqxjyZqk8cAJwI0AEbEjIn6fbVSZawDGSGoAxgLrM45n2DnpDL+pwBMFP6+lhn/JFpI0HTga+HW2kWTqauBvgZ6sAykD+wNdwDfS2403SGrOOqisRMQ64AvA48AG4PmIuD3bqIafk46VhKRxwI+Aj0bEpqzjyYKkdwIbI+LBrGMpEw3AMcBXIuJoYCtQs89AJU0kuSuyP9AONEv602yjGn5OOsNvHfC6gp/3TdtqlqRGkoTz7Yi4Oet4MvQW4N2S1pDcdn2rpG9lG1Km1gJrI6L3yveHJEmoVp0MPBoRXRHRDdwMvDnjmIadk87wewA4WNL+kppIHgTeknFMmZEkknv2yyPii1nHk6WIuCIi9o2I6ST/X9wVEVX3L9nBiogngSckHZo2vQ1YlmFIWXsceKOksenfm7dRhYUVDVkHUG0iYqekS4DbSKpPvh4RSzMOK0tvAf4MWCxpYdr29xHx0wxjsvLxYeDb6T/QVgPnZxxPZiLi15J+CDxEUvX5G6pwShxPg2NmZiXj22tmZlYyTjpmZlYyTjpmZlYyTjpmZlYyTjpmZlYyTjpWUySFpKsKfr5M0ieGaexvSvqj4RhrgPOck87IfHef9va05BZJMyW9fRjPOUHSh4qdy2wonHSs1mwHzpY0OetACqUTPA7WB4H/ExGzChsjYn1E9Ca9mcCQks4AMUwAXko6fc5lNmhOOlZrdpJ84e6v+u7oe6UiaUv650mS7pX0Y0mrJX1W0rmS7pe0WNKBBcOcLKlT0u/SudZ618/5vKQHJC2S9JcF4/5C0i0U+Sa+pDnp+Esk/Wva9o/A8cCNkj7fp//0tG8T8CngTyQtlPQnkpolfT2N+TeSzkiPOU/SLZLuAu6UNE7SnZIeSs/dO0P6Z4ED0/E+33uudIzRkr6R9v+NpFkFY98sab6khyV9ruDz+GYa62JJr/pvYdXLMxJYLfoSsKj3l+AgHQUcDjxL8s35GyLiWCWL0n0Y+GjabzrJ8hYHAndLOgh4P8mMwW+QNAr4paTe2YOPAY6IiEcLTyapHfhX4PXAc8Dtks6MiE9JeitwWUR0Fgs0InakyakjIi5Jx/sMybQ7fy5pAnC/pDsKYjgyIp5Nr3bOiohN6dXg/6ZJ8fI0zpnpeNMLTnlxctr4A0mHpbEeku6bSTKz+HZgpaRrgSnA1HTNGNJ4rEb4SsdqTjrL9X+QLJg1WA+kawNtBx4BepPGYpJE0+umiOiJiIdJktNhwGzg/ek0QL8GJgEHp/3v75twUm8A7kknf9wJfJtk7Zk9NRu4PI3hHmA0sF+67+cR8Wz6WsBnJC0C7iBZlmPvAcY+HvgWQESsAB4DepPOnRHxfERsI7mam0byuRwg6VpJpwE1Oet4rfKVjtWqq0nmuPpGQdtO0n+ISaoDmgr2bS943VPwcw+v/HvUd16pIPlF/uGIuK1wh6STSKbzLwUB74mIlX1iOK5PDOcCrcDrI6JbyYzYr2XJ5MLPbRfQEBHPSToKOBW4EPhj4M9fwzmsgvhKx2pS+i/7m0geyvdaQ3I7C+DdQOMeDH2OpLr0Oc8BwEqSyV8vUrLEA5IO0cCLld0PnChpspIl0OcA9w4hjs1AS8HPtwEflqQ0hqP7OW48yZo/3emzmWn9jFfoFyTJivS22n4k77uo9LZdXUT8CPgHans5g5rjpGO17CqgsIrtayS/6H8LvIk9uwp5nCRh/Ay4ML2tdAPJraWH0ofvX2WAuwwRsYHkOcrdwG+BByPix0OI424g31tIAHyaJIkukrQ0/bmYbwMdkhaTPItakcbzDMmzqCV9CxiALwN16THfB85Lb0P2ZypwT3qr71vAFUN4X1bhPMu0mZmVjK90zMysZJx0zMysZJx0zMysZJx0zMysZJx0zMysZJx0zMysZJx0zMysZP4/vfvzieU5qnUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_exp_f1 = np.max(exp_f1['accuracies'])\n",
        "print('Best F1 Accuracy->',best_exp_f1)\n",
        "best_exp_f4 = np.max(exp_f4['accuracies'])\n",
        "print('Best F4 Accuracy->',best_exp_f4)\n",
        "best_exp_f8 = np.max(exp_f8['accuracies'])\n",
        "print('Best F8 Accuracy->',best_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e29ab8e-5188-477c-d361-2b5580610cf1",
        "id": "zsIBbcJHkUKy"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 Accuracy-> 99.99923270897777\n",
            "Best F4 Accuracy-> 99.9931621130439\n",
            "Best F8 Accuracy-> 85.19250046885993\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "worst_exp_f1 = np.min(exp_f1['accuracies'])\n",
        "print('Worst F1 Accuracy->',worst_exp_f1)\n",
        "worst_exp_f4 = np.min(exp_f4['accuracies'])\n",
        "print('Worst F4 Accuracy->',worst_exp_f4)\n",
        "worst_exp_f8 = np.min(exp_f8['accuracies'])\n",
        "print('Worst F8 Accuracy->',worst_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14ac6456-22d9-48d6-fabe-a49feaa08597",
        "id": "UIXfnU1LkUKy"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Worst F1 Accuracy-> 76.70760753422495\n",
            "Worst F4 Accuracy-> -443.49406273689755\n",
            "Worst F8 Accuracy-> 84.83609177296375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_exp_f1 = np.median(exp_f1['accuracies'])\n",
        "print('median F1 Accuracy->',median_exp_f1)\n",
        "median_exp_f4 = np.median(exp_f4['accuracies'])\n",
        "print('median F4 Accuracy->',median_exp_f4)\n",
        "median_exp_f8 = np.median(exp_f8['accuracies'])\n",
        "print('median F8 Accuracy->',median_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e335363a-6b71-4b0f-9d2e-cef00f9e8572",
        "id": "G6pQ_dJrkUKy"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "median F1 Accuracy-> 99.9300235473201\n",
            "median F4 Accuracy-> 84.81563124395565\n",
            "median F8 Accuracy-> 84.98026273495664\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import mean \n",
        "t1 = mean(exp_f1['timedRuns'])\n",
        "t4 =  mean(exp_f4['timedRuns'])\n",
        "t8 =  mean(exp_f8['timedRuns'])\n",
        "print(t1,t4,t8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWXbCRdlkwZu",
        "outputId": "6f2104dd-21f9-45b7-9036-4d04cfaad1b2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "502.3042 5052.7664 37.357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp_f1 = mean(exp_f1['accuracies'])\n",
        "accuracy_exp_f1 = mean(exp_f1['accuracies'][0:20])\n",
        "efficiency_exp_f1 = mean(exp_f1['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp_f1)\n",
        "print(\"Accuracy\",accuracy_exp_f1)\n",
        "print(\"Efficiency\",efficiency_exp_f1)"
      ],
      "metadata": {
        "id": "mT7ivGV5Nc6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e42289a-87f3-4ade-b831-c372450aba3c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 97.15072062113802\n",
            "Accuracy 98.75955444692738\n",
            "Efficiency 502.3042\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp_f4 = mean(exp_f4['accuracies'])\n",
        "accuracy_exp_f4 = mean(exp_f4['accuracies'][0:20])\n",
        "efficiency_exp_f4 = mean(exp_f4['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp_f4)\n",
        "print(\"Accuracy\",accuracy_exp_f4)\n",
        "print(\"Efficiency\",efficiency_exp_f4)"
      ],
      "metadata": {
        "id": "61XYvS4-NdeK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e84895d2-ae07-4ba3-cccc-2bce4864f51e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 44.69882462219997\n",
            "Accuracy 38.63674774507583\n",
            "Efficiency 5052.7664\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp_f8 = mean(exp_f8['accuracies'])\n",
        "accuracy_exp_f8 = mean(exp_f8['accuracies'][0:20])\n",
        "efficiency_exp_f8 = mean(exp_f8['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp_f8)\n",
        "print(\"Accuracy\",accuracy_exp_f8)\n",
        "print(\"Efficiency\",efficiency_exp_f8)"
      ],
      "metadata": {
        "id": "2euGxBTpNdvw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a59e1f4a-c187-41fc-db09-439731a728a4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 85.00608714632948\n",
            "Accuracy 84.99969833455413\n",
            "Efficiency 37.357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment 2"
      ],
      "metadata": {
        "id": "wNlYkVSbguml"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "LdQ5JuKNOMlX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F1\n",
        "## Hyperparameters\n",
        "print('F1')\n",
        "OBJECTIVE_FUNC=f1\n",
        "VECTOR_LENGTH = 10\n",
        "BOUNDS= (-100,100)\n",
        "NUM_PARTICLES =100\n",
        "ITERATIONS=50\n",
        "NUM_INFORMANTS=2\n",
        "W = 0.1  # inertia constant\n",
        "C1 = 0.1   # cognative constant\n",
        "C2 = 1    # social constant\n",
        "exp_f1 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS,NUM_PARTICLES,ITERATIONS,NUM_INFORMANTS,W,C1,C2,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BG1XA_GiXTYm",
        "outputId": "ee7c8e6d-ec45-4a4c-f7da-7e9956ee882f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1\n",
            "RUN  1\n",
            "Time taken-> 1592.4270000000001 ms\n",
            "Fittest individual-> [-39.31168686651435, 58.899776295256565, -46.32252599315081, -74.65189640897033, -16.79948574457866, -80.54424339792071, -10.593104171619295, 24.968974667346796, 89.83858485627843, 9.112043287267074]\n",
            "Accuracy -> 99.99999984611055 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 1607.116 ms\n",
            "Fittest individual-> [-39.31190708363487, 58.899906246435876, -46.322365465623115, -74.65151813668284, -16.79973181302789, -80.5440948186807, -10.593485234281344, 24.969412990832215, 89.83839254859782, 9.111903469398984]\n",
            "Accuracy -> 99.9999999993102 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 1596.06 ms\n",
            "Fittest individual-> [-39.3118946527735, 58.899926111925396, -46.32238688082779, -74.65149962352282, -16.799788484834856, -80.5441134598417, -10.593450637098151, 24.969463371629136, 89.83837635348674, 9.11181413997136]\n",
            "Accuracy -> 99.9999999948273 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 1583.817 ms\n",
            "Fittest individual-> [-39.31205055987303, 58.89982129483433, -46.32247858632017, -74.65149184306229, -16.799855344455278, -80.54401404369293, -10.593372343305036, 24.969340336318254, 89.83845365295298, 9.111962789472434]\n",
            "Accuracy -> 99.99999997926608 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 1547.509 ms\n",
            "Fittest individual-> [-39.31198911965811, 58.8998371099744, -46.322404796177224, -74.65148502173166, -16.799746952568558, -80.54405547960454, -10.593507991513189, 24.969482415807107, 89.83830895613403, 9.11188770939149]\n",
            "Accuracy -> 99.99999999297162 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 1525.243 ms\n",
            "Fittest individual-> [-39.3119006309777, 58.89992499122198, -46.32231323587397, -74.65150295152448, -16.799619911078135, -80.5441611226246, -10.593581924900107, 24.969346802308223, 89.83842454556633, 9.111872230287732]\n",
            "Accuracy -> 99.99999999350506 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 1626.539 ms\n",
            "Fittest individual-> [-39.31187012387829, 58.899916830714446, -46.32236089493509, -74.65154206729187, -16.799665697809676, -80.54414083671877, -10.593550694418408, 24.96944884607404, 89.83835841916601, 9.111882901603101]\n",
            "Accuracy -> 99.99999999682308 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 1582.104 ms\n",
            "Fittest individual-> [-39.3119077298697, 58.89991559357955, -46.32261895966317, -74.65138286461965, -16.799772449492686, -80.54399969751769, -10.593478858872546, 24.969514126330363, 89.83833930875434, 9.111862028302095]\n",
            "Accuracy -> 99.99999997869479 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 1591.2869999999998 ms\n",
            "Fittest individual-> [-39.31189712756097, 58.90003841194789, -46.32249247680662, -74.65159280803634, -16.799800336412225, -80.54419449940838, -10.59357765650613, 24.969456847695547, 89.83849677550643, 9.111817065447994]\n",
            "Accuracy -> 99.99999998203673 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 1542.673 ms\n",
            "Fittest individual-> [-39.31180130212941, 58.89986053014699, -46.32245374372873, -74.65152435964535, -16.799684386415624, -80.5440273002308, -10.593447981309698, 24.969404059445637, 89.83840401576543, 9.111864805828214]\n",
            "Accuracy -> 99.99999999460286 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 1541.411 ms\n",
            "Fittest individual-> [-39.311889541191476, 58.89992361163967, -46.322445349141645, -74.65153496245652, -16.799689282454477, -80.54414247283218, -10.593526682824097, 24.96924903371876, 89.83849339399498, 9.111775289834686]\n",
            "Accuracy -> 99.99999998807945 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 1564.117 ms\n",
            "Fittest individual-> [-39.311858547167816, 58.89990311934286, -46.322437848802466, -74.65154855076022, -16.799642002674666, -80.54399433110304, -10.593475838974904, 24.96943839206765, 89.83837525677119, 9.111922401660445]\n",
            "Accuracy -> 99.99999999484018 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 1551.2189999999998 ms\n",
            "Fittest individual-> [-39.311876245711616, 58.899955830525975, -46.322414746750034, -74.65133260850664, -16.799856233539337, -80.54417212363994, -10.593542347706729, 24.969542202774207, 89.8383891976666, 9.112040685072596]\n",
            "Accuracy -> 99.99999997701036 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 1532.797 ms\n",
            "Fittest individual-> [-39.31189810400228, 58.899806076847256, -46.32219797420782, -74.65143758913256, -16.799474992189392, -80.54396184950879, -10.593571250289187, 24.96930637870874, 89.83829724283049, 9.111945727426484]\n",
            "Accuracy -> 99.99999996672437 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 1548.1799999999998 ms\n",
            "Fittest individual-> [-39.31187725415081, 58.899886452538325, -46.322408555925634, -74.65154792352887, -16.799710278146794, -80.54409412974637, -10.593452981076465, 24.96936114933006, 89.8384599537692, 9.111963488393965]\n",
            "Accuracy -> 99.99999999676528 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 1567.0059999999999 ms\n",
            "Fittest individual-> [-39.31189184432856, 58.899942038850284, -46.322344653240094, -74.65148813628264, -16.79975849538082, -80.54414501106872, -10.593563763372687, 24.969402703030426, 89.83841062431644, 9.111889391375778]\n",
            "Accuracy -> 99.99999999671466 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 1475.012 ms\n",
            "Fittest individual-> [-39.31187860540322, 58.89995832706964, -46.32255241146837, -74.65156806484413, -16.799669738855155, -80.54406634896941, -10.593448891357273, 24.9694152628383, 89.8383586169426, 9.11184692970964]\n",
            "Accuracy -> 99.99999999085688 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 1582.074 ms\n",
            "Fittest individual-> [-39.31181208769169, 58.89993989420554, -46.322389435279895, -74.65138770027332, -16.79967811552007, -80.5439588926156, -10.593290607496794, 24.969329971072156, 89.83837825788821, 9.112036015247988]\n",
            "Accuracy -> 99.99999997552104 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 1511.096 ms\n",
            "Fittest individual-> [-39.31132659244345, 58.900071817556935, -46.32235019004762, -74.65145221143638, -16.799206291773164, -80.54394527377147, -10.59308088411579, 24.969443108131316, 89.83885100887055, 9.1123448954877]\n",
            "Accuracy -> 99.99999973119384 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 1573.65 ms\n",
            "Fittest individual-> [-39.31196057223883, 58.899949093377934, -46.3223778184106, -74.65140139836414, -16.79961130197139, -80.54413047364189, -10.593534191484638, 24.969432268570053, 89.83836628393516, 9.11180950032964]\n",
            "Accuracy -> 99.99999999186073 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 1564.796 ms\n",
            "Fittest individual-> [-39.3118848057787, 58.89988192289103, -46.32238032951046, -74.65148727783613, -16.799632825163613, -80.54419974474771, -10.593488144869912, 24.96941640226022, 89.83842487700099, 9.11196085338317]\n",
            "Accuracy -> 99.999999995489 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 1589.0 ms\n",
            "Fittest individual-> [-39.31187086468544, 58.899897727009524, -46.32239142873512, -74.65147896605286, -16.799654516291564, -80.54410323047996, -10.593475964496657, 24.969421151212, 89.83838489694956, 9.11185970254638]\n",
            "Accuracy -> 99.99999999859416 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 1584.276 ms\n",
            "Fittest individual-> [-39.3119478808278, 58.89994339316989, -46.32236835811734, -74.65146779193059, -16.79968470840025, -80.54406540967268, -10.593575445575313, 24.9693448937582, 89.83837072456063, 9.111908717274353]\n",
            "Accuracy -> 99.99999999615417 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 1510.971 ms\n",
            "Fittest individual-> [-39.31162989003229, 58.899785122733334, -46.32228864577896, -74.65179506381091, -16.7995921371219, -80.54408958447854, -10.593611651467212, 24.96905408825163, 89.83853752150537, 9.112125273749518]\n",
            "Accuracy -> 99.99999991130159 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 1543.774 ms\n",
            "Fittest individual-> [-39.31190750760605, 58.89983647329237, -46.32241771106551, -74.65153725758366, -16.799694936028924, -80.54408500706134, -10.593515538830234, 24.969419866356585, 89.83841171746408, 9.111941172816977]\n",
            "Accuracy -> 99.99999999810825 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "9QyZhRuKOOjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F4\n",
        "## Hyperparameters\n",
        "print('F4')\n",
        "OBJECTIVE_FUNC=f4\n",
        "VECTOR_LENGTH = 10\n",
        "BOUNDS= (-100,100)\n",
        "NUM_PARTICLES =400\n",
        "ITERATIONS=30\n",
        "NUM_INFORMANTS=20\n",
        "W = 0.1  # inertia constant\n",
        "C1 = 0.1   # cognative constant\n",
        "C2 = 1    # social constant\n",
        "exp_f4 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS,NUM_PARTICLES,ITERATIONS,NUM_INFORMANTS,W,C1,C2,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3MOqgy9X0bY",
        "outputId": "bd92501f-f56b-4db6-e21f-b86933899a9e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F4\n",
            "RUN  1\n",
            "Time taken-> 13787.626 ms\n",
            "Fittest individual-> [35.8521463412404, -83.40280770929608, -10.285746273351867, -83.84617581039672, 83.45024900559876, 46.863479946699464, -89.3129424581939, -27.54478140183817, 76.16818005474668, -39.05508347484456]\n",
            "Accuracy -> 99.95744632217921 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 13639.366 ms\n",
            "Fittest individual-> [35.604664278196985, -82.87973493214264, -10.641836166308032, -83.606780897697, 83.32076768823597, 46.76255431122189, -89.29801425425067, -27.5132883656051, 76.2498432608139, -39.168953339081405]\n",
            "Accuracy -> 99.98696409375822 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 13743.771999999999 ms\n",
            "Fittest individual-> [35.65683121343811, -82.87290344271068, -10.725960015277044, -83.56609462784063, 83.13777803693878, 47.04331491214133, -89.39211236641965, -27.396714883368332, 76.10051464613689, -39.06300344011717]\n",
            "Accuracy -> 99.99782445019339 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 14262.795 ms\n",
            "Fittest individual-> [35.64935804655061, -82.9406803413143, -10.640143066128056, -83.56911107494074, 83.12426316181069, 47.07564958397283, -89.43820461325406, -27.420386742835568, 76.17256082442184, -39.089469436608255]\n",
            "Accuracy -> 99.9994897819715 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 13677.099999999999 ms\n",
            "Fittest individual-> [37.356277234171095, -85.40348884437128, -4.631709693071366, -99.97901680904472, 99.99914706792546, 40.79395124036956, -87.98629141294461, -28.25944005698239, 75.6787243772019, -38.42181524460296]\n",
            "Accuracy -> 57.959166547439146 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 13714.643 ms\n",
            "Fittest individual-> [35.42861876459925, -82.47853262307234, -10.923090181451421, -83.57089899390874, 83.30655663338477, 47.06955471567878, -89.68432696240791, -27.31223974039616, 76.16043016508713, -39.126023153610504]\n",
            "Accuracy -> 99.96530202138683 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 13684.784 ms\n",
            "Fittest individual-> [35.928213602844146, -83.4503723659298, -10.284817921878897, -83.5977372889958, 83.07448301897922, 47.11115943806858, -89.53059631800751, -27.571536448182915, 76.50172185635775, -39.29735786930256]\n",
            "Accuracy -> 99.9397588007765 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 13599.994999999999 ms\n",
            "Fittest individual-> [35.62620682705968, -82.9298999040124, -10.625733387429166, -83.57897116931517, 83.14695573653596, 47.04607132179362, -89.43140152046385, -27.382716704858215, 76.11652699899902, -39.07148254457248]\n",
            "Accuracy -> 99.99958040414738 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 13647.584 ms\n",
            "Fittest individual-> [35.34756288711707, -82.56488613341784, -10.414869099822132, -84.52560549776194, 83.84044381857234, 46.48576791180797, -88.71829840874709, -27.928216239893484, 76.75674030650613, -39.641387211376426]\n",
            "Accuracy -> 99.72446774817757 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 13678.268 ms\n",
            "Fittest individual-> [35.59932442908981, -82.86802046853478, -10.629751384451197, -83.63295532034918, 83.16847095191315, 47.061600520598994, -89.41232888032032, -27.476545917613432, 76.18841464171472, -39.07948101781873]\n",
            "Accuracy -> 99.99903229234437 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 13652.98 ms\n",
            "Fittest individual-> [35.271944023320955, -82.20836347870447, -11.608402238522398, -82.65097437245055, 82.46180416664336, 47.380745020576356, -89.4979190919808, -27.380754481048097, 76.22578525169965, -38.95525770168879]\n",
            "Accuracy -> 99.79357495117642 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 13659.122 ms\n",
            "Fittest individual-> [36.030951251652404, -83.49666487736364, -11.894082490745616, -80.85116464801254, 81.42556526957246, 52.97177122179476, -99.96177029171372, -23.626225665310645, 78.22302879636332, -40.10928690754289]\n",
            "Accuracy -> 86.24062670879668 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 13740.458999999999 ms\n",
            "Fittest individual-> [35.61890540578902, -82.86734788296751, -10.70516806817027, -83.54395442932628, 83.15664864037431, 47.02541204664287, -89.43097568780605, -27.424334441824055, 76.12227606261959, -39.030983126066666]\n",
            "Accuracy -> 99.99920144065152 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 13605.581 ms\n",
            "Fittest individual-> [35.62791783686646, -82.91809946767304, -10.644792556026186, -83.57502499833782, 83.15714539655943, 47.04384208478091, -89.43128034219365, -27.422755377464707, 76.14449330640232, -39.06516673957237]\n",
            "Accuracy -> 99.99997162613562 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 13654.785 ms\n",
            "Fittest individual-> [35.28765386160777, -82.59963608781007, -10.355244293195101, -83.5206397242763, 81.79282375573449, 53.94902015884144, -99.99990596542436, -23.92567344384118, 77.86584783434418, -39.60028029993595]\n",
            "Accuracy -> 86.75941574871999 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 13633.793000000001 ms\n",
            "Fittest individual-> [35.62595468517389, -82.91433985800052, -10.635875934040287, -83.59793072908192, 83.16810833935904, 47.04758986441319, -89.43945003861822, -27.421339896899163, 76.15822766167327, -39.06753287069704]\n",
            "Accuracy -> 99.99991369169594 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 13760.135999999999 ms\n",
            "Fittest individual-> [35.68799839860021, -83.00577290872651, -10.517077312438472, -83.68168421402217, 83.1773060740403, 47.053791937652846, -89.43508303179019, -27.50293878832303, 76.22190196324745, -39.045483078733334]\n",
            "Accuracy -> 99.99563498170978 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 13715.673999999999 ms\n",
            "Fittest individual-> [35.468890119253714, -83.13943067591805, -10.119879376361666, -83.44918620849568, 82.16210837027668, 53.137552514135095, -99.41819412818298, -23.092298961682403, 76.27630606629151, -38.230576400435815]\n",
            "Accuracy -> 88.54682860858236 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 13705.299 ms\n",
            "Fittest individual-> [35.57577576247322, -82.81266470315161, -10.713995305864659, -83.5622160434712, 83.15385518815906, 47.038630158576694, -89.41495031057859, -27.427196722201792, 76.14267364109635, -39.048604479351056]\n",
            "Accuracy -> 99.99859190678978 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 13929.853 ms\n",
            "Fittest individual-> [35.564739852758834, -82.81638648354615, -10.744116102395123, -83.47731853797633, 83.15229991810772, 47.006231144874036, -89.39281130992691, -27.446551653515797, 76.16477964222638, -39.11272398243208]\n",
            "Accuracy -> 99.99515457059188 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 16433.98 ms\n",
            "Fittest individual-> [35.639214448386205, -82.94067211380207, -10.616144845338578, -83.57935346353618, 83.14110857704912, 47.056599105711435, -89.43589366518404, -27.423330718595544, 76.1345497144134, -39.063928359869045]\n",
            "Accuracy -> 99.99972178146174 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 13743.114 ms\n",
            "Fittest individual-> [36.06944121528647, -83.57303239808311, -10.371928139324192, -83.64209008799502, 83.14840866063119, 47.19843278999878, -89.65952261880653, -27.474523859727512, 76.56560363275835, -39.4330949534332]\n",
            "Accuracy -> 99.91551938002227 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 13676.113000000001 ms\n",
            "Fittest individual-> [37.336334027964696, -87.42862497586219, -6.118692356992926, -88.4308027910302, 88.99962243145161, 43.920535073890186, -87.80204638996993, -40.98108127989629, 99.51889546631222, -49.685390104220666]\n",
            "Accuracy -> 31.802629287423457 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 13692.331 ms\n",
            "Fittest individual-> [34.63578115529162, -80.16419862977122, -5.180879618123176, -99.99681584604875, 93.95225177750716, 44.45234884259935, -88.4961860250302, -27.27735914484984, 75.63700939029364, -38.67900952840995]\n",
            "Accuracy -> 67.87977660857372 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 13804.837 ms\n",
            "Fittest individual-> [35.0239580337588, -80.64665529610606, -5.231625306799707, -99.93752590276725, 94.60618026717592, 44.286969474517356, -89.84908701428695, -25.52469709636616, 75.39255399541199, -39.60403979250224]\n",
            "Accuracy -> 67.44011250198855 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "vkBnPP23OQ1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F8\n",
        "## Hyperparameters\n",
        "print('F8')\n",
        "OBJECTIVE_FUNC=f8\n",
        "VECTOR_LENGTH = 10\n",
        "BOUNDS= (-32,32)\n",
        "NUM_PARTICLES =20\n",
        "ITERATIONS=10\n",
        "NUM_INFORMANTS=2\n",
        "W = 0.1  # inertia constant\n",
        "C1 = 0.1   # cognative constant\n",
        "C2 = 1    # social constant\n",
        "exp_f8 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,BOUNDS,NUM_PARTICLES,ITERATIONS,NUM_INFORMANTS,W,C1,C2,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kn1sARtiY_Is",
        "outputId": "99cc409b-5c5d-41d9-8824-b6337025bb24"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F8\n",
            "RUN  1\n",
            "Time taken-> 54.137 ms\n",
            "Fittest individual-> [3.7008622919967067, 16.835265342382556, -30.41646390663736, -26.214455214877596, 22.423556160583253, 27.69543781897093, 17.245557108363073, -25.36345673740351, -6.098897041008129, -11.849796212679173]\n",
            "Accuracy -> 84.96540871243033 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 54.019 ms\n",
            "Fittest individual-> [24.475021533937195, 7.645319539914247, -13.960634354498236, 7.4195066872248585, -1.3118764248243906, -24.727549404668395, 7.720137764541729, 23.810241110057582, -20.66859624583244, 11.968743132224027]\n",
            "Accuracy -> 85.11960257738875 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 74.764 ms\n",
            "Fittest individual-> [3.7627900065124154, -8.373118513340746, 27.96844716338668, 15.581542340510472, -21.399322143765293, 2.2573494524931172, -10.508455975807383, -15.982648442827722, 8.590791619050956, -9.450336317835728]\n",
            "Accuracy -> 85.05183317483566 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 57.276 ms\n",
            "Fittest individual-> [13.820683610171425, 30.215982934531283, 0.3157099626220713, -29.75917299731661, -24.684370917798784, 26.555355780498743, 5.362711566127633, 4.210206465107122, -17.415268060559367, 28.24199635874143]\n",
            "Accuracy -> 85.1313058715654 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 56.008 ms\n",
            "Fittest individual-> [31.801961059120302, 12.564445313477052, 16.176927602361587, -20.150831497385052, -8.842540530056178, -24.38225642881354, -6.67193634846975, 22.62837271509865, 31.814924586229253, 4.7473754575249]\n",
            "Accuracy -> 85.07990730360949 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 74.162 ms\n",
            "Fittest individual-> [-13.273340874405434, -20.86092652751231, 8.505163453895896, 19.02583170630601, 23.599774624835497, -30.004398004376036, -23.041616588706905, -7.950002311539001, -28.28585701685554, -14.423852077112421]\n",
            "Accuracy -> 85.07178248079364 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 85.768 ms\n",
            "Fittest individual-> [1.7013681516626384, -23.32924975192439, -5.359897322401846, 16.435762434282935, 4.309105803902765, -13.050744094326495, 14.680782698429127, -10.180659442374875, -9.784014666046625, 13.861916840094352]\n",
            "Accuracy -> 84.97255843234781 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 57.167 ms\n",
            "Fittest individual-> [-11.956285655720416, 11.094062878147398, 12.83940087044367, 14.213739982358852, 2.1631014976335226, 4.483701927040656, 5.176169782039463, -5.528020125007799, 3.37622710959144, -7.359204102107826]\n",
            "Accuracy -> 85.05823879493738 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 80.603 ms\n",
            "Fittest individual-> [29.379296665320155, 3.9047231152300554, 14.971208455175574, 16.562955947390897, -1.8188459083945223, 6.839667447965017, -9.013316244812156, 17.885986390879577, -20.48207196163731, 15.508997820759847]\n",
            "Accuracy -> 84.9814895379593 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 98.155 ms\n",
            "Fittest individual-> [7.6017759178195945, -8.308861102034696, -8.639817409119118, 1.5286050175279016, -32, -0.43518816672942773, 12.219476097830679, -9.28985444256779, 28.434762703449984, 4.930624434805679]\n",
            "Accuracy -> 85.03792792556008 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 51.35 ms\n",
            "Fittest individual-> [-4.290392699289363, -11.378500015072643, -8.075145138855321, -3.30602753494286, -8.723538176844524, 7.149368565992198, -18.508016805855576, -6.341037565922669, 0.3274093784192935, 14.10404967334291]\n",
            "Accuracy -> 85.01766876401162 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 53.591 ms\n",
            "Fittest individual-> [-21.689614484214854, 16.560019282036926, 22.738802781893476, -27.917391483069448, -18.738080682034923, 24.274359850588038, -12.90454529772789, -8.254855057362471, -9.292007333282498, 2.84343356299363]\n",
            "Accuracy -> 84.94754148372475 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 73.491 ms\n",
            "Fittest individual-> [-30.19777774272904, -21.801353155844293, -5.020285959924128, -16.683116945290465, -32, -25.243302434860922, 10.380791155203049, 25.157546641610114, 11.185772612052432, -21.715210303997985]\n",
            "Accuracy -> 85.06633925242805 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 66.319 ms\n",
            "Fittest individual-> [-32, -29.746456381164364, -20.07492829844516, -7.420101989933953, -3.7359440688251935, 0.9783977233759287, -20.450631291983367, -32, 8.331807571678212, -23.206115509618556]\n",
            "Accuracy -> 85.16618581779257 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 62.66 ms\n",
            "Fittest individual-> [-3.9315204546627562, -1.1761883000590627, 32, 16.042638240868847, 3.997130736491943, 16.143728809316613, 9.970025690855191, -6.8264104874530105, -32, 3.4374095304113452]\n",
            "Accuracy -> 85.08441227140385 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 80.619 ms\n",
            "Fittest individual-> [2.357267859410586, 1.9761807762832946, 1.6754035352915428, 4.082788990987764, 10.322407613483055, 22.63638886296403, 32, 14.590157690396378, -32, -28.49195754170152]\n",
            "Accuracy -> 85.16102529998355 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 75.81400000000001 ms\n",
            "Fittest individual-> [31.67833742611239, 24.926392998673236, 13.07207934103794, 27.051758786666767, -29.84632109492057, 13.247277962637645, -31.630068378804854, 2.4725047924771895, 22.14832968772325, -8.241800202961954]\n",
            "Accuracy -> 85.10651385354758 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 75.729 ms\n",
            "Fittest individual-> [-0.5584685685971154, -32, -30.098317214254845, 18.66957051437025, -13.917588732037883, 22.620426916946094, 18.168474571601323, -5.034984125501976, -13.460181791702057, 32]\n",
            "Accuracy -> 85.07112555202197 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 85.226 ms\n",
            "Fittest individual-> [20.730236836088846, -6.113527737108435, 20.105752922869, 3.767489176182761, 7.875015128037087, 20.436083205818083, -1.34265181233881, 8.795139517485012, 5.547458232530513, -9.028002950694255]\n",
            "Accuracy -> 85.16456081958201 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 75.898 ms\n",
            "Fittest individual-> [-1.456781628290999, -8.1983215377187, -4.904106280762295, -14.644106372781433, 5.417506072986852, 12.450146323273138, -32, 22.40947975324193, -32, -14.960887882947365]\n",
            "Accuracy -> 85.07100221920028 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 82.094 ms\n",
            "Fittest individual-> [-0.7145067898530533, 9.222951917405938, 22.316810471750507, 10.935394881721395, 2.4269037836905607, 15.147677851482992, -4.718924669868359, -2.418700844914477, -9.099295345336103, 31.649595560607523]\n",
            "Accuracy -> 85.03610507759142 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 85.919 ms\n",
            "Fittest individual-> [23.567816879891645, 3.38108058943521, 32, 14.961036796901157, -27.058030490003063, -23.389951524993844, -4.704150878567795, -17.954587549561417, -14.679608498363695, -23.32249778273702]\n",
            "Accuracy -> 85.01903461287432 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 51.947 ms\n",
            "Fittest individual-> [23.83874745931359, 6.68856406333539, -6.522194529558753, 10.221700088252817, 32, -25.117081161391425, -5.328944441726824, 13.736254066065989, 18.436456029895925, 18.859824397867364]\n",
            "Accuracy -> 84.93369676503717 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 74.333 ms\n",
            "Fittest individual-> [-15.160064957556218, -8.263820928056573, 24.358860852054708, -24.62142100573523, -2.296025690228766, -30.739596108293206, -24.255873929434507, 1.0778047285943146, 14.75414438862186, 24.78232898862851]\n",
            "Accuracy -> 84.91602464363443 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 65.933 ms\n",
            "Fittest individual-> [13.552802542767722, 18.61274405238856, -1.683986380003346, 21.238132242147362, -15.357201745089847, 23.219722402919093, 24.243150358146764, -0.8204861315653237, -31.256861750366493, -27.60211853870212]\n",
            "Accuracy -> 85.08853669589041 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Compare F1,F4,F8 Accuracy\n",
        "\n",
        "x_axis = [i for i in range(experiment_runs)]\n",
        "plt.plot(x_axis, exp_f1['accuracies'])\n",
        "plt.plot(x_axis, exp_f4['accuracies'])\n",
        "plt.plot(x_axis, exp_f8['accuracies'])\n",
        "\n",
        "plt.title('Accuracy vs Runs')\n",
        "plt.xlabel('Runs')\n",
        "plt.ylabel('Accuracy(%)')\n",
        "plt.legend(['F1', 'F4','F8'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "rJ-akTEWZgk_",
        "outputId": "69b1617c-18a5-4f82-944d-ea631f8838c4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BVGdDtSWIchRWZ7muXa56KZ/etJPVgjPcSSDfaqa9L8O+LbA0z85xY4nG3DZf+Vl+pZstT7jEF2T1km/GcW4okXU/zO1y7J49ruRx4odcSaQSWAnClJ2wOstZJ7nDRWvguAtdH4ahQ7mv/5KXWI4LYZKnZZe6k3w+DcIb10D9tMI67Y1n5dWw9yXY8Xz26/zxHlfyqJTqJRhOEFXaWc4SRDlqmRNOI7V1ksts2Wr3C3HrU7mvu+lhN4ZSGJ/rgjPcST7XaqbBfle9tOSi8H6pL7/MXda77ofZvV/VVS/NO8WVQCqFNVKbstM8253Mg7722sZhyuzos9ywzrlWM3W+Cbv+EHz1ki9VzfRobtVMr/4CDnW7/hRhibe47W+4P7srrXY870oclVR6APd3IVFrgzBlpLkNNAG9eVwlMpaBbnfSsARxuJo4HPM+eOknuSXlTd5osPkOzpeNZZe6mfFyaUTfuMa1qyz8k/DiAneyH+jKblTcdT90JY7ll4UbU9BEqnrAPksQ5aglhImDrJPc+Jatdgn5zd9mv85Lj7jJh6YvCi+uhWe6q6OyrWYaGnDjSy25OPyG4KPeA1MXTlzNdOigK2ksv7Q8Z3KcSBUP2GcJohylph4NMEFYJ7nxLT4PYvHsq5l69rpJh8KqXvJFa1xbwss/dSf/ibz6S/erPszqJZ8IrPiIGxV3/9ax37fpYRfTio+EH1MYqnjAPksQ5Sg19WiAneWsk9z46prcOEqbHnZDZ0zk5X8HtDhzGSz7gKsezKaaaeOD7hdv2NVLvhVXAQLr7xz7Pet+CFMXuBJHJbIqJlNWmmaCRIK9kqm7yueizsbSVdC1A3ZmcenmS49A61Ew6/jw41p4pjtJTVTNNHQIXv6Ju+S2kCHHczFlHiw62yWITIn17ddh69Ow4urK7X9TxbPKVeg3NslFom5OgECrmHa52bvqmoLb5mRz3PluyIxND43/vv4uN3vc0lXFGY00VuvaFCaqZnrtSfdLtxjVS+lWXg0HtsHrvzr8tT/cBYhX0qhQVoIwZae5LeAqJuskN6H6qa5qZuND4/cQ3vwYJA4Vd6rMZZfAwAGXBMay8UH3I+Do9xYpKM+Si9xnN3oAv2TCTSu66GxX0qhU1khdXCLyaRHZICIvishnvGXTRORxEdns3Qc4sE0FCrqznHWSy87SVbD/ddi9Yez3bHrYlfDmnVq8uI4+C+qmjD0EeGLQVXsddyHE6ooXF7j9nfBB97mkT+H6+q+ga3vl9X0Yrb7VDQs/2F/qSIqu6AlCRI4HPgGcCpwIXCwii4EvAk+o6jHAE97z6tU8O9g5IayTXHaWXOzaf8a6mmmw3w19cdyFxa1Tj9XCkgtdEsg0JMjrv3L15GGMvZSNlVe7k+gL9w0vW3e7q78/7sLSxBSU1HhM1VeKKEUJYinwjKoeVNUh4FfAZcAlwG3ee24DilyRWmaa29wfZCHDUPuSSejpsASRjaYZMP/drpopk9eedB3Xwr68NZNll7i68Ex1/S+ugdpmV51TCm0nwuwThquZDr7tOhK+4wrXEbGSxat3uI1SJIgNwBkiMl1EGoALgSOBWarqt8p2ALMyrSwi14nIWhFZu3dvgD2Ny02QU48efMsNkmad5LKzdBXs3QRvbT78tU0Pu6qeBWcWP65FZ7sksHHU1Uyp6qULSnsyXvlR2LUeOja4jnGJgcqvXoLhBFGF7RBFTxCqugm4GXgMeBRYDyRGvUeBjK2EqnqLqraravuMGTPCDrd0UlOPBpAgrJNcbvzSweirmRJDrv/Dse8v3mWk6WJ1Lgm89JORc0JvfdrV/Zeqesl3wgchWgvr73AlidknuJJFpaviAftK0kitqt9X1ZNV9UxgP/AKsFtE2gC8+z2liK1sNAc43IZ1ksvNlHkw9+TDq5ne/A30vV3cq5dGW36pSwbp1UwvrnGDyi0+p3RxATRMc1c0PXerK0ms/Ghp4wmKtUEUl4jM9O7n49of7gQeAq7x3nINUOCM7RXO/7UfZIKwq5iyt3S1O8ntf2N42aZH3HAcpTwRLzrHJQP/aqbEkKteOvZ8qKkvXVy+FVe7qVKjta5EMRlYG0TR3S8iG4GHgetVtRP4CnCeiGwGzvWeV6/4FKhpCKiKaZe7MqexAiaJLxd+KcEfqVTVPV50DtQ2li6umrhLBpsecdVMb/waDu4rffWSb9FZbliNZZe6EsVk4A8wWIUliIAnq82Oqh42zZWq7gNKXEYuIyJeZ7kgShA7XXIIem7iyWz6IjeMxsaH4F3Xu+E3unbA2f+j1JF58zDcB1t/7dpJahrLZ47nSBSue9KVtCaLWJ0bqtwaqU1ZCSxBWCe5vCxdDduecZ/fpkfcxDHHvr/UUcHic11S2HC/u6rq2PeVR/WSr35qecUThCod0dUSRDlraQums5x1ksvPstWAV7W06WFYeEZ5VJvU1LtEtf5ON4fFsuruMlQU8SlVWcVkCaKcNbe5X6/jjQuUDRuHKT8zlsD0xfCbb8G+zeHOHJer5Ze6WQdrGtxseCZccStBmHLT3OY6G6WPb5OrwX63vlUx5U7EVTPtf909X3JRaeNJt/g8dzXTMe+D2oZSRzP5VemAfZYgyllLAL2pU30gLEHkZdlqdz/vlPLqiV7bAB//KVz4D6WOpDpUaRuEXdZSzprTelPPWp7fNixBFKZthbvkdfkHSh3J4dreUeoIqkeVtkFYgihnqfGYCmioTnWSK6Nfv5VEBK68feL3mckt3uomikomK3dmvDxUfYJIJBMMJAboT/QzMOTdJwboH3L36Y8VJSpRd4u4+1gkNu7zhCZIJBMMJYfcTYdSjxOattx7LZlMIiJEJUpEk0SbGons+g3R12cQkYhbPupeRBBvZjNh1H3HcxCvg74O6OhJLY9FYtREa6iJuFssEks9Tl/ub3/0Z5Z+HIPJwZHH4R2LqrrPIxIlJrHUZxOLxFI3/7OKSSy1H1UloQmSmkzdDyWHRjxPJBMkNIF6Dfj+Z5D6z9tWRCIjngsy5veVi6QmOZQ4xKHkIQ4lDjGUHHLP05YNJgdTn43/eDBx+HP/cVKTw5+NxA77nPzvKH2ZoqnvI5FMMJgcTP1djf6e/L9FESEWiY34O4pIhJjEiERG/m1FJUokEiGCe4+IjHws3mvee/xlSU26Y9WhkZ+Dvyztc/Bji0hkxN/i6PvRy/xjyBSTIKnjSv17IgJC6u9h9N/KiOVpy2KRGLHaemKixPo7iRbpSrakJukf6qc/0U//UD99Q33D94l+Fk1ZRFtTuDUDooVeIVNC7e3tunbt2pzXu2PTHXxr3bfoT/QzlBwKIbLJxf8HOZgcJJFMoJnHUSyYf8JLaoa5jUMmSCpRZUoeiWRi+MTvneRMdUolDe9WE6kZkdCjkejhyWdUQvJ/qPnJbEiHRpz8/R+l4/nyO7/MlUuuzO8YRJ5T1faJ3leVJYhFrYu4ZPEl1EXriEfj1MXqqIvWUR+rP2yZ/zgejSMiJJLeL1rvF1vq15pfUkhbnki6X0X+H41/oh3xK1oO/zWd1OTwr+XbLyfRPIvkRf/nsF/V/r1/QvWT/YgT+K9uRt/aDJd9N7Xc/9U5+lfsiF+2o5YPJYdG/AMY/Y9i9K1GakBIlZ5SpSXvF22qVKUjS1P+iTr1i9b/JRsZWXLyfz1GJJI69tTngKKqpP7Tkc/TSyAJTaQSX6bvMP3YayI11EZr3S1SS020htpIbWpZ6vXI8PP00tiIX8HRw5cBqb+t9NLZ6JJA+ufmn6yi4r6TaCQ68m9Mhr8zf7n/N3NYqcw7/hElN69U639uqXvv806STG3PX57QxIi/75pozfB9htKB/z7/807/WxxdQh39d4qSiiE9ptRj/5b2Hv9vYqy/Ff/vyf8vkUww1PFHhv5wF4OnXMtQ4/SMJeb07yv1by1tf/5/7v/h5UmS1ERqqI/WE4/FU7f05/Wx+sOWzWsKfxrXqkwQp7Wdxmltp5U6jOw0z4Oe3dB6dH7r9/ZCw1xoK+L0mCZvUYkSJUpdNLxpQ/2kWhstwZDl4/CTWZwyHKaj/il46l9g7tmwsARzgZRI9bS2VKrm2YUN2Ged5IwpXJWO6GoJotw1z3HDKaRPEJMtVRuHyZgg+HNCVFlnOUsQ5a6lDVBXzZSrvv0w1G8lCGMKVaWzylmCKHfNBUw9ap3kjAlGbTMgVddZzhJEuWsuYLgN6yRnTDAiETdxkJUgTFkpJEH4pQ5/+lJjTP6qcERXSxDlrmE6RGrymxfCqpiMCU4VjuhqCaLcRSLD80LkqnuXSzCx8K6pN6ZqVOGIrpYgKkHz7PwG7LOZ5IwJThWO6FqSBCEinxWRF0Vkg4jcJSJxEVkoIs+IyBYRuUdEyqubZym1tOV5FZN1kjMmMNYGET4RmQv8JdCuqscDUeBDwM3A11V1MbAfuLbYsZWt5jl5VjFZJzljAmNtEEUTA+pFJAY0ALuAs4H7vNdvA2wmdl/zbDjUDQPd2a+TGISePVaCMCYo9a0w1AdD44+yOpkUPUGo6g7ga8CbuMRwAHgO6FRNjaG8HZibaX0RuU5E1orI2r179xYj5NLz+zHkUs3UsxtQSxDGBKUKx2MqRRXTVOASYCEwB2gEzs92fVW9RVXbVbV9xowZIUVZZvLpC9FlneSMCZQ/HpMliFCdC7yuqntVdRD4MfAeoNWrcgKYB+woQWzlKZ8E0W2d5IwJlJUgiuJN4DQRaRA33dI5wEbgl8Dl3nuuAR4sQWzlyT/J59JZLpUgrARhTCCqcETXUrRBPINrjH4eeMGL4RbgBuBzIrIFmA58v9ixla26Jqhrye1Kpq6drgd2w/Tw4jKmmqRGdK2eBFGSGeVU9UbgxlGLXwNs2rOxNLfl1lmuu8OVPCLWF9KYQKTaIKonQdjZo1K05DjchnWSMyZY1gZhylZzjr2pu3ZZJzljglQTh2idtUGYMtTcBj0dkExm9/7uDitBGBO0KhuwzxJEpWiZA8khOPjWxO8d6HY9ry1BGBOsKhuwzxJEpcjlUlfrJGdMOOJTrARhypDfnyGbznLWSc6YcFTZiK5ZJwgRaRSRaJjBmHG05NCb2jrJGROOKhvRdcwEISIREfmwiPxERPYALwG7RGSjiPyDiCwuXpiGxpkgkeyuZPKroawEYUywrJE65ZfAIuBLwGxVPVJVZwKnA78DbhaRq4sQowGIxlySyKazXHeH63ld1xR+XMZUE78NQrXUkRTFeD2pz/UG0xtBVd8G7gfuF5Ga0CIzh2uenV1nOeskZ0w44q2gCTjUA3XNpY4mdGOWIEYnB29a0P8sIn8hItMzvceErGVOllVM1knOmFBU2YB9uVzF9E/AIdx0oGvCCceMq7kty0Zq6yRnTCjqq2u4jfEaqe8SkUVpi6YB9+Kql6aGHZjJoLkN+t6Gwf6x35NMuh7XliCMCV6VDdg3XgnivwN/JyL/KCKtuGlCHwB+CtxUhNjMaNlc6tq71/W4tk5yxgSvymaVG7ORWlVfAz4sIqcD9wA/AS5S1USxgjOjpGaW64BpCzO/xzrJGROeKhvRdbwqpqkicj2wDPggru3hZyKyqljBmVFSCWKcS12tk5wx4bFG6pQ1QCegwA9V9YfAKmCliDxcjODMKH4V03hXMvmd5OwqJmOCZ1VMKdNxU4PWA38OoKp9wP8UETv7lEK8FWL147dBdHe4HteNM4sXlzHVIhJ1nVCrpJF6vARxI/AokAC+mP6CquYwc40JjIjXWW68BLHTJYdoSWaTNWbyq6IB+8ZrpL4fd0mrKScTdZazTnLGhKuKBuwbr5H6uyJy/BivNYrIfxKRj+S6QxE5TkTWp926ROQzIjJNRB4Xkc3evfW1yGTCEoT1gTAmVFU0YN94jdT/DPyNiGwSkXtF5Nsi8q8i8jTwG6AZ10aRE1V9WVVXqOoK4GTgIK5/xReBJ1T1GOAJRlVrGY/fm3qswcJsHCZjwlVFs8qNV8W0HrhCRJqAdqAN6AM2qerLAe3/HOBVVX1DRC4B3ustvw14ErghoP1MHi1zYKjf/YHWjypkDfZB336rYjImTFU0q1w2LZlnAT9R1WQI+/8QcJf3eFZa43cHMCvTCiJyHXAdwPz580MIqcylph7ddVPiyAEAABcmSURBVHiC8Ed6tRKEMeGpokbqbAbruxLYLCJfFZElQe1YRGqB1bjxnUZQVcX1vziMqt6iqu2q2j5jxoygwqkcqalHM3SWS3WSswRhTGjiU9xw34nJP5j1hAlCVa8GVgKvAreKyG9F5DoRKXQw9AuA51V1t/d8t9+/wrvfU+D2J6eWtOE2Rkt1krNe1MaEJjWia1dp4yiCrIb7VtUuXIP03bi2iA8Az4vIXxSw76sYrl4CeAi4xnt8DfBgAduevJrSqphGS1Ux2ThMxoSmikZ0nTBBiMhqEXkA12hcA5yqqhcAJwKfz2enItIInAf8OG3xV4DzRGQzcK733IxWE4f6aWNXMcXqhwcUM8YELzVg3+RPENk0Uv8p8HVVfSp9oaoeFJFr89mpqvbihvJIX7YPd1WTmUjLnLGrmFraXI9rY0w4gh6wb/dG6HjBlfyb26B5lhvOowz+HWeTIG4CUvUZIlKPu+Joq6o+EVZgZhzNs4fbG9JZJzljwhf0rHIPXg87nx+5rKZhOGE0zfISR1oCaW6DlrlQ2xBMDGPIJkHcC7w77XnCW3ZKKBGZiTW3uV8co3XvhLntxY/HmGoSdBvE/tdh+Qeg/Vr3I6+nw91373L3u9bDK4/C4MGR613wD/DO64KJYQzZJIiYqh7yn6jqIe8SVVMqLXOgZw8khoYH5VN1DddLrQRhTKiCHPJ7oMd1bp19Aiw8Y+z3qcJA93Di6NkNc1YWvv8JZJMg9orIalV9CMDr8fxWuGGZcTXPBtT9kUyZ65b17YfEgFUxGRO2mgaI1ASTIA5sc/etR43/PhGIt7jbjGML32+WskkQnwTuEJFvAQJsAz4WalRmfKnOch3DCcI6yRlTHCLBjeja6SWIKUcWvq0QTJggVPVV4DRvTCZUtSf0qMz4/H4O3Ttx4x0y3C/COskZE76gRnQ98Ka7b63QBAEgIhcBy4G4eJdeqer/DDEuMx4/CaR3lkuVIKyTnDGhC2pE185trrqqqTz/3WbTUe7/4cZj+gtcFdMHgQkqzEyoGo6ASGzkvBBWxWRM8QQ1YN+Bba6aOJLVoBZFl01U71bVjwH7VfVvgXcBxWslMYeLRNwvjvQE0bUTGqZDrK50cRlTLYJsgyjT9gfILkH0e/cHRWQOMIgbj8mUUkvbyM5y1knOmOIJrA1iG7SW77QF2SSIh0WkFfgH4HlgK3BnmEGZLDS3jRxuw2aSM6Z4/DaIsWZ2zMbQIe9KxAotQYhIBDcNaKeq3o9re1iiqn9TlOjM2PypR31du2wmOWOKJT4FkkOH927ORdd2QMv2CiaYIEF4s8j9c9rzAVWtjqmUyl1LGwx0uZ6YiUHo3WslCGOKJR7AeExl3gcCsqtiekJE/lSkDIYWNMPSO8v17AbUEoQxxRLEiK6pXtTlmyCy6Qfx58DngCER6cdd6qqq2hJqZGZ86Z3lYvXusXWSM6Y4ghjRtXMbINAyL5CQwpBNT+pCpxY1YWhJK0HE4u6xdZIzpjiCGNH1wDb3bzZWvmOfTpggROTMTMtHTyBkisxPBl07ocYrQTRbCcKYogikDeLNsm5/gOyqmP4q7XEcOBV4Djg7lIhMduqaobbZm2Y07rrrN0yfeD1jTOH8BFFoG8Tck4OJJyTZVDGtSn8uIkcC3wgtIpO9lrbheaibZ5dtd31jJp1C54RIJuHADlh2aXAxhSCrwfpG2Q4sDToQk4fm2a7/Q03crmAyppiiMahtyr8NoqcDkoNlfQUTZNcG8U3A7y4YAVbgelTnzeuZ/T3geG/b/wl4GbgHWIDrrX2Fqu4vZD+TXvMceOM/XBXTrGWljsaY6hKfkn8JotMb5ntK+Q6zAdn1g1iLa3N4DvgtcIOqXl3gfv8JeFRVlwAnApuAL+J6bR8DPOE9N+NpaRuegtBKEMYUVyEjunaWfx8IyK6K6T6gX1UTACISFZEGVc2rj7mITAHOBP4M3BzXwCFvKtP3em+7DXgSuCGffVSN5jZXTD00aAnCmGIrZERXf6KgMr+KKaue1EB92vN64OcF7HMhsBf4gYisE5HviUgjMEtV/cGFOoBZmVYWketEZK2IrN27d28BYUwC6UnBOskZU1yFjOjauQ3qp0JdU7AxBSybBBFPn2bUe9xQwD5jwEnAd1R1JdDLqOokVVWG2z0Y9dotqtququ0zZswoIIxJID0pWCc5Y4qrkFnlynyYb182CaJXRE7yn4jIyUBfAfvcDmxX1We85/fhEsZuEWnz9tEG7ClgH9UhPSlYJzljiqvQNogyr16C7BLEZ4B7ReRpEfk17kqjT+W7Q1XtALaJyHHeonOAjcBDwDXesmuAB/PdR9VomoUbGgsrQRhTbPEpbkTlZCK39VQrpgSRTUe534vIEsA/ob+sqoMF7vcvgDtEpBZ4Dfg4Lln9SESuBd4ArihwH5NftAYaZ8BQf9nXZRoz6aQP2NcwLfv1Dr7t5pGogBJENv0grgfuUNUN3vOpInKVqn47352q6nqgPcNL5+S7zarV0gaD/RO/zxgTrPQB+3JJEP4VTGV+iStkV8X0CVVNtcR4ndc+EV5IJifvuBJOvLLUURhTffIdbqMCJgryZdMPIioi4l1ZhIhEgfIdn7bavOv6UkdgTHXKd0TX1ERBk6ANAngUuEdE/sV7/ufAT8MLyRhjKkC+s8p1boOaRtcPosxlkyBuAK4DPuk9/yNgl8wYY6pbvrPKHdjm2h8qYBbnCdsgVDUJPIMbQO9U3DwQm8INyxhjyly+s8pVwERBvjFLECJyLHCVd3sL1/8BVT2rOKEZY0wZq20CieZXgpiX6SLO8jNeFdNLwNPAxaq6BUBEPluUqIwxptyJ5D5g30AP9O2vmBLEeFVMlwG7gF+KyHdF5BxS3XaNMcbkPCdEBV3BBOMkCFVdo6ofApYAv8QNuTFTRL4jIu8rVoDGGFO26ltza4OooD4QkF0jda+q3unNTT0PWIfN02CMMXmUICqnFzVk15M6RVX3e8Nt25AYxhgTb82tDaJzG0RqoKkyegrklCCMMcakyacNYspciFTGqbcyojTGmHKU66xyFTIPhM8ShDHG5Cs+BRIDMJjlHGoVMg+EzxKEMcbkK5cB+4YGoHuXlSCMMaYq5DJg34Ht7r5CrmACSxDGGJO/XEoQFdZJDixBGGNM/lIjumZRgqiwTnJgCcIYY/KXy6xyB7YBAi1zQw0pSJYgjDEmX34VUzZtEJ3boLkNYpUzIWc2EwYFTkS2At1AAhhS1XYRmYYbUnwBbu6JK7z5r40xpjzlWoKooAZqKG0J4ixVXaGq/sDoXwSeUNVjgCe858YYU75itVDTkGUbROVMFOQrpyqmS4DbvMe3AZeWMBZjjMlOfMrECSKZgK4dVoLIkgKPichzInKdt2yWqu7yHncAszKtKCLXichaEVm7d+/eYsRqjDFji2cx3EZ3BySHKq4EUZI2COB0Vd0hIjOBx0XkpfQXVVVFRDOtqKq3ALcAtLe3Z3yPMcYUTTazylVgHwgoUQlCVXd493uAB4BTgd0i0gbg3e8pRWzGGJOTbEZ0rcA+EFCCBCEijSLS7D8G3gdsAB4CrvHedg3wYLFjM8aYnGUzq1yFTRTkK0UV0yzgARHx93+nqj4qIr8HfiQi1wJvAFeUIDZjjMlNtiWI+mlQ21icmAJS9AShqq8BJ2ZYvg+wmeqMMZUl3gr9XZBMjj0RUAX2gYDyuszVGGMqT3wKoDDQNfZ7KmyiIJ8lCGOMKUT9BCO6qlbcREE+SxDGGFOI1HAbYzRUH3wbBg9aCcIYY6rORHNCVOgVTFC6jnKhGRwcZPv27fT395c6lJzF43HmzZtHTU1NqUMxxmRrolnlKrQPBEzCBLF9+3aam5tZsGAB3qW0FUFV2bdvH9u3b2fhwoWlDscYk62JRnSt0F7UMAmrmPr7+5k+fXpFJQcAEWH69OkVWfIxpqpNNKtc5zaoaYT6qcWLKSCTLkEAFZccfJUatzFVrbYZkLFLEJ1vuvaHCvz3PSkThDHGFE0kMv6AfQcqbx4InyWIEESjUVasWJG6bd26lX379nHWWWfR1NTEpz71qVKHaIwJ0njDbXRWZh8ImISN1OWgvr6e9evXj1jW29vL3/3d37FhwwY2bNhQosiMMaGoH2NOiIFu1zZRgZe4wiRPEH/78Its3DlO9/c8LJvTwo2rlue8XmNjI6effjpbtmwJNB5jTBkYa1a5Cr7EFSZ5giiVvr4+VqxYAcDChQt54IEHShyRMSZU8VZ465XDl1fwJa4wyRNEPr/0g5CpiskYM4mN1Ujd6fWirtAShDVSG2NMocZqpD6wDaK10DSr+DEFwBKEMcYUqr4VhvpgaGDk8s5t0DJ37HkiytykrmIqNwsWLKCrq4tDhw6xZs0aHnvsMZYtW1bqsIwxhUofsK9p5vDyCp0oyGcJIgQ9PT0Zl2/durW4gRhjisNPEH2dIxNE5zZYfG5pYgpAZZZ7jDGmnGQasG9oAHo6KroEYQnCGGMKlWlWuQPb3X2FXsEEJUwQIhIVkXUi8oj3fKGIPCMiW0TkHhGpLVVsxhiTk0yzyqX6QFiCyMengU1pz28Gvq6qi4H9wLUlicoYY3IVzzDkd4X3ooYSJQgRmQdcBHzPey7A2cB93ltuAy4tRWzGGJOzTLPKHdgGiLvMtUKVqgTxDeCvgaT3fDrQqapD3vPtQMZPVUSuE5G1IrJ279694UdqjDETqYlDtG5kG0TnNmhug1jl1pYXPUGIyMXAHlV9Lp/1VfUWVW1X1fYZM2YEHF0wMg337XvzzTdpamria1/7WukCNMYEr7718DaICm5/gNL0g3gPsFpELgTiQAvwT0CriMS8UsQ8YEcJYgvEeGMxfe5zn+OCCy4ockTGmNCNHm6j802Yd0rp4glA0ROEqn4J+BKAiLwX+IKqfkRE7gUuB+4GrgEeLHhnP/0idLxQ8GZGmH0CXPCVvFZds2YNCxcupLGxMdiYjDGlF28dboNIJqBrB7ReVtqYClRO/SBuAD4nIltwbRLfL3E8efOH+16xYgUf+MAHANe7+uabb+bGG28scXTGmFCklyC6OyA5VNFXMEGJh9pQ1SeBJ73HrwGnBrqDPH/pFypTFdNNN93EZz/7WZqamkoSkzEmZPWtsM+bEKzC54Hw2VhMRfLMM89w33338dd//dd0dnYSiUSIx+M2P7Uxk0X6rHKToA8EWIIomqeffjr1+KabbqKpqcmSgzGTSdybl1oVOt9wyyr8KqZyaoMwxpjKFZ8CmoSBblfF1DAdaiv7ghQrQYRgrOG+fTfddFNxAjHGFE/6iK6d2yq+egmsBGGMMcGoTxuPaRJ0kgNLEMYYE4z08Zg6t8GUyr6CCSxBGGNMMPwRXd9+zc1PbSUIY4wxwHAJwh+9wdogjDHGAMNtELs3uHsrQRhjjAGgrsXd737R3VsJwmSSabjvwcFBrrnmGk444QSWLl3K3//935c6TGNMkCJRqJsCA11Q2wT1U0sdUcGsH0QIMo3FdOeddzIwMMALL7zAwYMHWbZsGVdddRULFiwoTZDGmODFp8DAAVd6ECl1NAWb1Ani5mdv5qW3Xwp0m0umLeGGU2/IeT0Robe3l6GhIfr6+qitraWlpSXQ2IwxJRafAgeYFO0PYFVMocg03Pfll19OY2MjbW1tzJ8/ny984QtMmzatxJEaYwLlN1RPgvYHmOQliHx+6QchUxXTs88+SzQaZefOnezfv58zzjiDc889l6OPProkMRpjQuBf6molCJOLO++8k/PPP5+amhpmzpzJe97zHtauXVvqsIwxQYpPrhKEJYgimT9/Pr/4xS8A6O3t5Xe/+x1LliwpcVTGmEClShCVP8wGWIIomuuvv56enh6WL1/OKaecwsc//nHe8Y53lDosY0yQrA3CTCTTcN9NTU3ce++9JYjGGFM0yy8DBJpnlzqSQFiCMMaYoByxGP7kr0odRWCKXsUkInEReVZE/iAiL4rI33rLF4rIMyKyRUTuEZHaYsdmjDFmWCnaIAaAs1X1RGAFcL6InAbcDHxdVRcD+4Fr892BqgYSaLFVatzGmMmp6AlCHb+Svsa7KXA2cJ+3/Dbg0ny2H4/H2bdvX8WdbFWVffv2EY/HSx2KMcYAJWqDEJEo8BywGPhn4FWgU1WHvLdsB+aOse51wHXgLh0dbd68eWzfvp29e/eGEHm44vE48+bNK3UYxhgDlChBqGoCWCEircADQNYdAlT1FuAWgPb29sOKCTU1NSxcuDCoUI0xpmqVtB+EqnYCvwTeBbSKiJ+w5gE7ShaYMcaYklzFNMMrOSAi9cB5wCZcorjce9s1wIPFjs0YY8ywUlQxtQG3ee0QEeBHqvqIiGwE7haR/wWsA75fgtiMMcZ4pNKu9kknInuBN/Jc/QjgrQDDqTTVfPzVfOxQ3cdvx+4cpaozJlqhohNEIURkraq2lzqOUqnm46/mY4fqPn479tyO3QbrM8YYk5ElCGOMMRlVc4K4pdQBlFg1H381HztU9/HbseegatsgjDHGjK+aSxDGGGPGYQnCGGNMRlWZIETkfBF52Zt74ouljqeYRGSriLwgIutFZG2p4wmbiPyriOwRkQ1py6aJyOMistm7n1rKGMMyxrHfJCI7vO9/vYhcWMoYwyIiR4rIL0VkozfvzKe95dXy3Y91/Dl9/1XXBuH14H4FN8THduD3wFWqurGkgRWJiGwF2lW1KjoLiciZQA/wb6p6vLfsq8DbqvoV7wfCVFW9oZRxhmGMY78J6FHVr5UytrCJSBvQpqrPi0gzbvToS4E/ozq++7GO/wpy+P6rsQRxKrBFVV9T1UPA3cAlJY7JhERVnwLeHrX4EtycI1DA3CPlboxjrwqquktVn/ced+PGe5tL9Xz3Yx1/TqoxQcwFtqU9H3PuiUlKgcdE5Dlvbo1qNEtVd3mPO4BZpQymBD4lIn/0qqAmZRVLOhFZAKwEnqEKv/tRxw85fP/VmCCq3emqehJwAXC9Vw1RtdTVsVZTPet3gEW46X53Af9Y2nDCJSJNwP3AZ1S1K/21avjuMxx/Tt9/NSaIHcCRac+rau4JVd3h3e/BTdZ0amkjKondXh2tX1e7p8TxFI2q7lbVhKomge8yib9/EanBnRzvUNUfe4ur5rvPdPy5fv/VmCB+DxwjIgtFpBb4EPBQiWMqChFp9BqsEJFG4H3AhvHXmpQews05AlU294h/cvR8gEn6/YuI4KYM2KSq/yftpar47sc6/ly//6q7ignAu7TrG0AU+FdV/d8lDqkoRORoXKkB3Fwgd072YxeRu4D34oY63g3cCKwBfgTMxw0Xf4WqTrrG3DGO/b246gUFtgJ/nlYnP2mIyOnA08ALQNJb/N9w9fDV8N2PdfxXkcP3X5UJwhhjzMSqsYrJGGNMFixBGGOMycgShDHGmIwsQRhjjMnIEoQxxpiMYqUOwJhKISIJ3GWDMeB14KOq2lnaqIwJj5UgjMlen6qu8EZGfRu4vtQBGRMmSxDG5Oe3eIM8isiTItLuPT7CG1IdEfkzEfmxiDzqzT/wVW95VERuFZEN3twcny3VQRgzHqtiMiZH3pwi5+CGMpjICtxImgPAyyLyTWAmMDdtjobWsGI1phBWgjAme/Uisp7hYaIfz2KdJ1T1gKr2AxuBo4DXgKNF5Jsicj7QNe4WjCkRSxDGZK9PVVfgTvLCcBvEEMP/luKj1hlIe5wAYqq6HzgReBL4JPC9sAI2phCWIIzJkaoeBP4S+LyIxHCDnp3svXz5ROuLyBFARFXvB74MnBRSqMYUxNogjMmDqq4TkT/iRsf8GvAjb4a+n2Sx+lzgByLi/0D7UkhhGlMQG83VGGNMRlbFZIwxJiNLEMYYYzKyBGGMMSYjSxDGGGMysgRhjDEmI0sQxhhjMrIEYYwxJqP/D219bBTbGQVTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis"
      ],
      "metadata": {
        "id": "t-rcfpSrOTFF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "gAPFcCYeOV0p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F1 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f1['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f1['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aXS7NnuDdP6D",
        "outputId": "1bacaf52-52e5-4b8e-d6f5-48da5fc57cef"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "OsuXI1UsOXHw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F4 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f4['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f4['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "EBBdrKH5f3l8",
        "outputId": "bf87789d-a6fd-4405-ad23-8bba0380e9b8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "s05yTw6NOYaM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F8 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f8['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f8['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0XB92-tWf8Jx",
        "outputId": "86cff1d1-274e-4352-9972-a9a5e95b47b6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_exp_f1 = np.max(exp_f1['accuracies'])\n",
        "print('Best F1 Accuracy->',best_exp_f1)\n",
        "best_exp_f4 = np.max(exp_f4['accuracies'])\n",
        "print('Best F4 Accuracy->',best_exp_f4)\n",
        "best_exp_f8 = np.max(exp_f8['accuracies'])\n",
        "print('Best F8 Accuracy->',best_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrW1xbOMb2tA",
        "outputId": "ac148265-ff46-4b66-e9e1-0f20ec0f4e6b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 Accuracy-> 99.9999999993102\n",
            "Best F4 Accuracy-> 99.99997162613562\n",
            "Best F8 Accuracy-> 85.16618581779257\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "worst_exp_f1 = np.min(exp_f1['accuracies'])\n",
        "print('Worst F1 Accuracy->',worst_exp_f1)\n",
        "worst_exp_f4 = np.min(exp_f4['accuracies'])\n",
        "print('Worst F4 Accuracy->',worst_exp_f4)\n",
        "worst_exp_f8 = np.min(exp_f8['accuracies'])\n",
        "print('Worst F8 Accuracy->',worst_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE5c1aB3cP46",
        "outputId": "0dc1b4d2-981f-49a8-b371-32e82205b72e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Worst F1 Accuracy-> 99.99999973119384\n",
            "Worst F4 Accuracy-> 31.802629287423457\n",
            "Worst F8 Accuracy-> 84.91602464363443\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_exp_f1 = np.median(exp_f1['accuracies'])\n",
        "print('median F1 Accuracy->',median_exp_f1)\n",
        "median_exp_f4 = np.median(exp_f4['accuracies'])\n",
        "print('median F4 Accuracy->',median_exp_f4)\n",
        "median_exp_f8 = np.median(exp_f8['accuracies'])\n",
        "print('median F8 Accuracy->',median_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYW5rFz4cdvH",
        "outputId": "d239964e-179e-4ad4-9184-0df52a11ea10"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "median F1 Accuracy-> 99.99999999297162\n",
            "median F4 Accuracy-> 99.96530202138683\n",
            "median F8 Accuracy-> 85.06633925242805\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import mean \n",
        "\n",
        "t1 = mean(exp_f1['timedRuns'])\n",
        "t4 =  mean(exp_f4['timedRuns'])\n",
        "t8 =  mean(exp_f8['timedRuns'])\n",
        "\n",
        "print(t1,t4,t8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZzajuG6lGDu",
        "outputId": "833bb8c4-f9d2-4327-a8e1-b732bcf0d3a3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1561.36616 13833.7596 70.11928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp2_f1 = mean(exp_f1['accuracies'])\n",
        "accuracy_exp2_f1 = mean(exp_f1['accuracies'][0:20])\n",
        "efficiency_exp2_f1 = mean(exp_f1['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp2_f1)\n",
        "print(\"Accuracy\",accuracy_exp2_f1)\n",
        "print(\"Efficiency\",efficiency_exp2_f1)"
      ],
      "metadata": {
        "id": "WK3uqVGQOaXg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52a1f1d8-3f2a-4286-89d3-ae5ad8e31fca"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 99.99999997069449\n",
            "Accuracy 99.99999996838575\n",
            "Efficiency 1561.36616\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp2_f4 = mean(exp_f4['accuracies'])\n",
        "accuracy_exp2_f4 = mean(exp_f4['accuracies'][0:20])\n",
        "efficiency_exp2_f4 = mean(exp_f4['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp2_f4)\n",
        "print(\"Accuracy\",accuracy_exp2_f4)\n",
        "print(\"Efficiency\",efficiency_exp2_f4)"
      ],
      "metadata": {
        "id": "o4mPTP1vOiKS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddbe3741-60ac-499e-fa2b-8b642f392de2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 91.43582825026775\n",
            "Accuracy 95.9428973348612\n",
            "Efficiency 13833.7596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp2_f8 = mean(exp_f8['accuracies'])\n",
        "accuracy_exp2_f8 = mean(exp_f8['accuracies'][0:20])\n",
        "efficiency_exp2_f8 = mean(exp_f8['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp2_f8)\n",
        "print(\"Accuracy\",accuracy_exp2_f8)\n",
        "print(\"Efficiency\",efficiency_exp2_f8)"
      ],
      "metadata": {
        "id": "JyZA-WmqOjTN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e78adc69-6d37-44cf-e79c-20c11089b761"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 85.05279311760607\n",
            "Accuracy 85.0663215072562\n",
            "Efficiency 70.11928\n"
          ]
        }
      ]
    }
  ]
}