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run_geatpy.py
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# -*- coding=utf-8 -*-
# @File : run_geatpy.py
# @Time : 2022/10/15 10:43
# @Author : EvanHong
# @Email : [email protected]
# @Project : code
# @Description:
import json
import numpy as np
from ga.Algorithm import Algorithm
from ga.algorithms.Algo_TSP_CLSC import Algo_TSP_CLSC
from ga.Population import Population
from ga.problems.Classical_TSP import Classical_TSP
from ga.operators.Crossover.Recombination import Recombination
from ga.operators.Selection.ElitismSelection import ElitismSelection
from ga.operators.Mutation.SwapMutation import SwapMutation
from ga.Support import *
from typing import overload, Union
import datetime
import geatpy as ea
from ga.problems.SOEA_TSP import SOEA_TSP
from ga.problems.MOEA_TSP import MOEA_TSP
from ga.problems.MOEA_TSP_Timewindow import MOEA_TSP_Timewindow
class Info(object):
pos = [0.1, 0.15]
poc = [0.1, 0.15]
proportion = [0.4, 0.5]
pom = [0.1, 0.15, 0.2]
def run(algorithm):
root = "/content/drive/MyDrive/Github/GeneticAlgorithm"
pop_size = 100
chromo_len = 100
pop = Population(pop_size, chromo_len, root + "/data/TSPTW_dataset.txt", Encoding.P)
problem = Classical_TSP(100, 100)
pos_list = [0.05, 0.1, 0.15]
poc_list = [0.05, 0.1, 0.15]
proportion_list = [0.4, 0.5]
pom_list = [0.1, 0.15, 0.2]
n = 5
history = {}
for pos in pos_list:
for poc in poc_list:
for proportion in proportion_list:
for pom in pom_list:
total_distance = 0
temp=[]
for i in range(n):
alg = algorithm(problem, pop, 20000, pos, poc, proportion, pom)
obj, bestfit, best, dist_history = alg.run()
temp.append(dist_history)
distance = alg.problem.fitness_preimage(bestfit)
total_distance += distance
np.savetxt(
root + f"/output/res/{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}_{algorithm}_{pos}_{poc}_{proportion}_{pom}_{distance}.txt",
best, fmt='%i', delimiter=" ")
alg.draw(best,
save_path=root + f"/output/pics/{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}_{algorithm}_{pos}_{poc}_{proportion}_{pom}_{distance}.png")
avg_dist = total_distance / n
history[(pos,poc,proportion,pom)]=(avg_dist,temp)
with open(root+f"/output/history/history.json",'w') as fw:
json.dump(history,fw)
def run_SOEA_TSP():
root=""
saveDir="./geaty_output/SOEA_TSP/"
problem = SOEA_TSP("SOEA_TSP",root+"./data/TSPTW_dataset_profit.txt",0.5)
# 构建算法
algorithm = ea.soea_SEGA_templet(
problem,
ea.Population(Encoding='P', NIND=100),
MAXGEN=1000, # 最大进化代数
logTras=0) # 表示每隔多少代记录一次日志信息，0表示不记录。
algorithm.mutOper.Pm = 0.2 # 修改变异算子的变异概率
algorithm.recOper.XOVR = 0.9 # 修改交叉算子的交叉概率
# 求解
res = ea.optimize(algorithm,
verbose=False,
drawing=1,
outputMsg=True,
drawLog=True,
saveFlag=True,
dirName=saveDir)
# print(res)
print('The best objective value is: %s' % res['optPop'].ObjV[0][0])
print('The best variables are: ')
path=[]
for i in range(res['optPop'].Phen.shape[1]):
path.append(res['optPop'].Phen[0, i])
problem.getReferObjV()
pop_size = 100
chromo_len = 100
pop = Population(pop_size, chromo_len, "./data/TSPTW_dataset.txt", Encoding.P)
pop.init_info()
problem = Classical_TSP(1e6)
alg = Algo_TSP_CLSC(problem, pop, 100, 0.1, 0.1, 0.5, 0.2)
alg.draw(path,
save_path=saveDir+f"{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}_{res['optPop'].ObjV[0][0]}.png")
return res
def run_MOEA_TSP():
root=""
saveDir="./geaty_output/MOEA_TSP/"
problem = MOEA_TSP("MOEA_TSP",root+"./data/TSPTW_dataset_profit.txt")
# 构建算法
algorithm = ea.moea_NSGA2_templet(
problem,
ea.Population(Encoding='P', NIND=100),
MAXGEN=100, # 最大进化代数
logTras=0) # 表示每隔多少代记录一次日志信息，0表示不记录。
algorithm.mutOper.Pm = 0.2 # 修改变异算子的变异概率
algorithm.recOper.XOVR = 0.9 # 修改交叉算子的交叉概率
# 求解
res = ea.optimize(algorithm,
verbose=False,
drawing=1,
outputMsg=True,
drawLog=True,
saveFlag=True,
dirName=saveDir)
# print(res)
print('The best objective value is: %s' % res['optPop'].ObjV[0][0])
print('The best variables are: ')
path=[]
for i in range(res['optPop'].Phen.shape[1]):
path.append(res['optPop'].Phen[0, i])
problem.getReferObjV()
pop_size = 100
chromo_len = 100
pop = Population(pop_size, chromo_len, "./data/TSPTW_dataset.txt", Encoding.P)
pop.init_info()
problem = Classical_TSP(1e6)
alg = Algo_TSP_CLSC(problem, pop, 100, 0.1, 0.1, 0.5, 0.2)
alg.draw(path,
save_path=saveDir+f"{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}_{res['optPop'].ObjV[0][0]}.png")
return res
def run_MOEA_TSP_Timewindow():
root=""
saveDir="./geaty_output/MOEA_TSP_Timewindow/"
problem = MOEA_TSP_Timewindow("MOEA_TSP_Timewindow",root+"./data/TSPTW_dataset_profit.txt")
# 构建算法
algorithm = ea.moea_NSGA2_templet(
problem,
ea.Population(Encoding='P', NIND=100),
MAXGEN=1000, # 最大进化代数
logTras=0) # 表示每隔多少代记录一次日志信息，0表示不记录。
algorithm.mutOper.Pm = 0.2 # 修改变异算子的变异概率
algorithm.recOper.XOVR = 0.9 # 修改交叉算子的交叉概率
# 求解
res = ea.optimize(algorithm,
verbose=False,
drawing=1,
outputMsg=True,
drawLog=True,
saveFlag=True,
dirName=saveDir)
# print(res)
print('The best objective value is: %s' % res['optPop'].ObjV[0][0])
print('The best variables are: ')
path=[]
for i in range(res['optPop'].Phen.shape[1]):
path.append(res['optPop'].Phen[0, i])
pop_size = 100
chromo_len = 100
pop = Population(pop_size, chromo_len, "./data/TSPTW_dataset.txt", Encoding.P)
pop.init_info()
problem = Classical_TSP(1e6)
alg = Algo_TSP_CLSC(problem, pop, 100, 0.1, 0.1, 0.5, 0.2)
alg.draw(path,
save_path=saveDir+f"{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}_{res['optPop'].ObjV[0][0]}.png")
return res
if __name__ == '__main__':
# run_SOEA_TSP()
run_MOEA_TSP()
# run_MOEA_TSP_Timewindow()