main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fbe2e375",
   "metadata": {
    "id": "fbe2e375"
   },
   "source": [
    "# Introduction to Optimization - 89589 - Final Project\n",
    "## Bar-Ilan University, 2022\n",
    "## Metaheuristic Optimization Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25107a26",
   "metadata": {
    "id": "25107a26"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ad5cb73",
   "metadata": {
    "id": "6ad5cb73"
   },
   "outputs": [],
   "source": [
    "from Algorithms import HillClimbing, SimAnn, TabuSearch\n",
    "from Algorithms.utils import problemGenerator\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745d38fa",
   "metadata": {
    "id": "745d38fa"
   },
   "source": [
    "### Generate adjacency matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7fdae1e2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7fdae1e2",
    "outputId": "7afa9bcf-2c47-4ab0-f026-a65e941e06c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.17 ms, sys: 0 ns, total: 4.17 ms\n",
      "Wall time: 4.94 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "small_matrix = problemGenerator(10)\n",
    "medium_matrix = problemGenerator(25)\n",
    "large_matrix = problemGenerator(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b2731e",
   "metadata": {
    "id": "36b2731e"
   },
   "source": [
    "These are adjacency matrices, that represent graphs. \n",
    "The value of every cell (i,j) represents the cost of the edge between i and j.\n",
    "#### For illustration, this is what the small matrix looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f2a84d3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9f2a84d3",
    "outputId": "a51dbb87-07bd-4868-adde-6636de7b5805"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 48, 97, 60, 66, 83, 92, 15, 82, 70],\n",
      " [48, 0, 18, 13, 28, 21, 59, 46, 15, 14],\n",
      " [97, 18, 0, 36, 36, 86, 14, 19, 70, 28],\n",
      " [60, 13, 36, 0, 52, 87, 62, 58, 100, 99],\n",
      " [66, 28, 36, 52, 0, 72, 70, 62, 70, 44],\n",
      " [83, 21, 86, 87, 72, 0, 48, 68, 70, 18],\n",
      " [92, 59, 14, 62, 70, 48, 0, 41, 13, 71],\n",
      " [15, 46, 19, 58, 62, 68, 41, 0, 81, 81],\n",
      " [82, 15, 70, 100, 70, 70, 13, 81, 0, 83],\n",
      " [70, 14, 28, 99, 44, 18, 71, 81, 83, 0]]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint \n",
    "pprint(small_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79de3e9",
   "metadata": {
    "id": "c79de3e9"
   },
   "source": [
    "## Run Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4QHK0EEDPo_K",
   "metadata": {
    "id": "4QHK0EEDPo_K"
   },
   "source": [
    "### Running the three algorithms on small matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "79e1a5ec",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "79e1a5ec",
    "outputId": "b8ad2ffc-4aae-460d-9528-dd49ffbfd12a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hill Climbing cost on small matrix: 327\n",
      "CPU times: user 1.89 ms, sys: 0 ns, total: 1.89 ms\n",
      "Wall time: 1.89 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "HC_result = HillClimbing.run(small_matrix)\n",
    "print(\"Hill Climbing cost on small matrix:\", HC_result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "32478a29",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "32478a29",
    "outputId": "69d5047f-68e9-4373-e689-e7dd9c9d6db3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Annealing cost on small matrix: 294\n",
      "CPU times: user 13.3 ms, sys: 1e+03 ns, total: 13.3 ms\n",
      "Wall time: 13.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "SA_result = SimAnn.run(small_matrix)\n",
    "print(\"Simulated Annealing cost on small matrix:\", SA_result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5572317c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5572317c",
    "outputId": "28f97991-93dc-493c-9b76-356f537e283a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tabu Seach cost on small matrix: 287\n",
      "CPU times: user 5.16 s, sys: 11 ms, total: 5.17 s\n",
      "Wall time: 5.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "TS_result = TabuSearch.run(small_matrix)\n",
    "print(\"Tabu Seach cost on small matrix:\", TS_result[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tNjI6ZqgQqnY",
   "metadata": {
    "id": "tNjI6ZqgQqnY"
   },
   "source": [
    "### Results on small matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ldoPe-uAkRDB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 519
    },
    "id": "ldoPe-uAkRDB",
    "outputId": "8225648b-055f-408d-cdaf-5b1585190b9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1.95, 0.1, '5.18 s')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " \n",
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    "\n",
    "small_times = [0.00189, 0.0132, 5.18]\n",
    " \n",
    "# Set position of bar on X axis\n",
    "br1 = np.arange(3)\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(br1, small_times, color ='r', width = barWidth,\n",
    "        edgecolor ='grey', label ='Small Matrix')\n",
    " \n",
    "# Adding Xticks\n",
    "plt.xlabel('Algorithm', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Seconds', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r for r in range(3)],\n",
    "        ['Hill Climbing', 'Simulated Annealing', 'Tabu Search'])\n",
    "plt.text(-0.08, 0.1, \"0.00189 s\")\n",
    "plt.text(0.92, 0.1, \"0.0132 s\")\n",
    "plt.text(1.95, 0.1, \"5.18 s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "38-V_g1eQtFq",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "38-V_g1eQtFq",
    "outputId": "bb31638d-ee44-48c4-b32b-4fbb2b064871"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    "\n",
    "small_scores = [327, 294, 287]\n",
    "br1 = np.arange(3)\n",
    "plt.bar(br1, small_scores, color ='r', width = barWidth,\n",
    "        edgecolor ='grey', label ='Small Matrix')\n",
    "plt.xlabel('Algorithm', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Cost', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r for r in range(3)],\n",
    "        ['Hill Climbing', 'Simulated Annealing', 'Tabu Search'])\n",
    "plt.text(-0.03, 3, \"327\")\n",
    "plt.text(0.97, 3, \"294\")\n",
    "plt.text(1.97, 3, \"287\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jQgbInJQQwHK",
   "metadata": {
    "id": "jQgbInJQQwHK"
   },
   "source": [
    "### Running the three algorithms on medium matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "TPZ8h27uQxFm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TPZ8h27uQxFm",
    "outputId": "4abea22d-17fc-44d1-dfa0-2db52abaeeef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hill Climbing cost on medium matrix: 612\n",
      "CPU times: user 21.4 ms, sys: 0 ns, total: 21.4 ms\n",
      "Wall time: 24.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "HC_result_medium = HillClimbing.run(medium_matrix)\n",
    "print(\"Hill Climbing cost on medium matrix:\", HC_result_medium[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "T8GSGAANQ2E0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T8GSGAANQ2E0",
    "outputId": "74f0b762-1585-42c3-c00d-5c8d1b0679f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Annealing cost on medium matrix: 570\n",
      "CPU times: user 21.3 ms, sys: 0 ns, total: 21.3 ms\n",
      "Wall time: 22.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "SA_result_medium = SimAnn.run(medium_matrix)\n",
    "print(\"Simulated Annealing cost on medium matrix:\", SA_result_medium[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "LmVEyx2fQ6AN",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LmVEyx2fQ6AN",
    "outputId": "5364a4ff-df65-476a-b5b9-f91402f3995d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tabu Seach cost on medium matrix: 469\n",
      "CPU times: user 59.7 s, sys: 101 ms, total: 59.8 s\n",
      "Wall time: 59.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "TS_result_medium = TabuSearch.run(medium_matrix)\n",
    "print(\"Tabu Seach cost on medium matrix:\", TS_result_medium[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xEitWHRYRF0X",
   "metadata": {
    "id": "xEitWHRYRF0X"
   },
   "source": [
    "### Results on Medium matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "hIlD_R70RA_Z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "hIlD_R70RA_Z",
    "outputId": "5af465d4-52e6-48f5-8d60-0148126cf213"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    "\n",
    "medium_times = [0.0242, 0.0226, 59.8]\n",
    "br1 = np.arange(3)\n",
    "plt.bar(br1, medium_times, color ='g', width = barWidth,\n",
    "        edgecolor ='grey', label ='Medium Matrix')\n",
    "plt.xlabel('Algorithm', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Seconds', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r for r in range(3)],\n",
    "        ['Hill Climbing', 'Simulated Annealing', 'Tabu Search'])\n",
    "plt.text(-0.07, 3, \"0.0242 s\")\n",
    "plt.text(0.93, 3, \"0.0226 s\")\n",
    "plt.text(1.95, 3, \"59.8 s\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "N3leiY__RIlu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "N3leiY__RIlu",
    "outputId": "021177ed-dc62-4e00-a956-6288cfaa59c8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    "\n",
    "medium_scores = [612, 570, 469]\n",
    "br1 = np.arange(3)\n",
    "plt.bar(br1, medium_scores, color ='g', width = barWidth,\n",
    "        edgecolor ='grey', label ='Medium Matrix')\n",
    "plt.xlabel('Algorithm', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Cost', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r for r in range(3)],\n",
    "        ['Hill Climbing', 'Simulated Annealing', 'Tabu Search'])\n",
    "plt.text(-0.03, 5, \"612\")\n",
    "plt.text(0.97, 5, \"570\")\n",
    "plt.text(1.97, 5, \"469\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "B2XMwREJRJEz",
   "metadata": {
    "id": "B2XMwREJRJEz"
   },
   "source": [
    "## Running the three algorithms on the Large matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8yM1V2QGRLnr",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8yM1V2QGRLnr",
    "outputId": "57b2ef74-314b-4642-ad4d-742cf7b1f886"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hill Climbing cost on large matrix: 1028\n",
      "CPU times: user 287 ms, sys: 2.99 ms, total: 290 ms\n",
      "Wall time: 292 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "HC_result_large = HillClimbing.run(large_matrix)\n",
    "print(\"Hill Climbing cost on large matrix:\", HC_result_large[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "G0VON6BoRPkE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G0VON6BoRPkE",
    "outputId": "a396f9fb-6419-452c-c5bb-4234bb17994f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Annealing cost on large matrix: 890\n",
      "CPU times: user 25.9 ms, sys: 2 ms, total: 27.9 ms\n",
      "Wall time: 29.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "SA_result_large = SimAnn.run(large_matrix)\n",
    "print(\"Simulated Annealing cost on large matrix:\", SA_result_large[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "tFMEq3aSRU-Z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tFMEq3aSRU-Z",
    "outputId": "c89cad1a-a717-4dcf-8db4-b201228e9818"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tabu Seach cost on large matrix: 672\n",
      "CPU times: user 4min 36s, sys: 427 ms, total: 4min 37s\n",
      "Wall time: 4min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "TS_result_large = TabuSearch.run(large_matrix)\n",
    "print(\"Tabu Seach cost on large matrix:\", TS_result_large[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "X_RVj0jnlZRx",
   "metadata": {
    "id": "X_RVj0jnlZRx"
   },
   "source": [
    "### Large Matrix Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "rQh7ekMPlbcG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "rQh7ekMPlbcG",
    "outputId": "5115fd92-a581-4584-dae6-4eb6e1cf5bd6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    "\n",
    "large_times = [0.292, 0.0299, 277]\n",
    "br1 = np.arange(3)\n",
    "plt.bar(br1, large_times, color ='cyan', width = barWidth,\n",
    "        edgecolor ='grey', label ='Large Matrix')\n",
    "plt.xlabel('Algorithm', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Seconds', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r for r in range(3)],\n",
    "        ['Hill Climbing', 'Simulated Annealing', 'Tabu Search'])\n",
    "plt.text(-0.07, 3, \"0.292 s\")\n",
    "plt.text(0.93, 3, \"0.0299 s\")\n",
    "plt.text(1.95, 3, \"277 s\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1NQQOPQklblJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "1NQQOPQklblJ",
    "outputId": "87355c7b-b790-40ee-b7a7-c5c3794fcbfe",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    "\n",
    "large_scores = [1028, 890, 672]\n",
    "br1 = np.arange(3)\n",
    "plt.bar(br1, large_scores, color ='cyan', width = barWidth,\n",
    "        edgecolor ='grey', label ='Large Matrix')\n",
    "plt.xlabel('Algorithm', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Cost', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r for r in range(3)],\n",
    "        ['Hill Climbing', 'Simulated Annealing', 'Tabu Search'])\n",
    "plt.text(-0.05, 10, \"1028\")\n",
    "plt.text(0.97, 10, \"890\")\n",
    "plt.text(1.97, 10, \"672\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Main.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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