遗传算法解决旅行商问题-优快云博客

本文使用遗传算法实现旅行商问题的求解，通过Python编程优化城市路线，降低旅行总距离。文章详细介绍了遗传算法的初始化、交叉、变异等步骤，并展示了优化过程和最终结果。

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib as mt
R = 6371  # 地球距离

# 参数
citys = 34  # 染色体DNA长度
pc = 0.1
pc = 0.2  # 交叉概率
pc = 0.3  # 交叉概率
pm = 0.12  # 变异概率
popsize = 500  # 种群规模
iternum = 1000  # 迭代次数
Fitness_choice = [100**0,100**1,100**2,(100**3)/2,100**3,2*100**4]
def Setdistance(file = 'D:/python/pycharm/pythonProject1/file/x_y.txt'):

    fi = open(file,'rt',encoding = 'UTF-8');txt = fi.readlines()
    spots = [];X_Y = []
    for i in range(len(txt)):
        txt[i] = txt[i].rstrip('\n')
        if i%2 == 0:
            spots.append(txt[i])
        else:
            X_Y.append(txt[i])
    for i in range(len(X_Y)):

        x_y =[eval(i) for i in X_Y[i].split(',')]
        X_Y[i] = x_y
    global cities

    cities = pd.DataFrame({'spot':spots,'x_y':X_Y})
Setdistance()
fi = open('distance.csv','rt');Distance = np.array([[eval(j) for j in i.strip('\n').split(',')] for i in fi.readlines()])

class GA(object):
    def __init__(self, citys, pc, pm, popsize, ):
        self.citys = citys
        self.pc = pc
        self.pm = pm
        self.popsize = popsize

        self.pop = np.vstack([np.random.permutation(citys) for _ in range(popsize)])

    def translateDNA(self, DNA, city_position):
        lineX = np.empty_like(DNA, dtype=np.float64)
        lineY = np.empty_like(DNA, dtype=np.float64)

        for i, d in enumerate(DNA):
            city_coord = city_position[d]
            lineX[i, :] = city_coord[:, 0]
            lineY[i, :] = city_coord[:, 1]
        return lineX, lineY
    def getdist(self,x_y1,x_y2):
        return R * np.arccos(np.cos(np.radians(x_y1[1])) * np.cos(np.radians(x_y2[1])) * np.cos(np.radians(x_y1[0]- x_y2[0])) + np.sin(np.radians(x_y1[1])) * np.sin(np.radians(x_y2[1])))
    def getFitness(self, lineX, lineY):
        totalDis = np.empty((lineX.shape[0],), dtype=np.float64)
        for j, (xval, yval) in enumerate(zip(lineX, lineY)):
            sum = 0
            for i in range(len(xval)-1):
                sum += self.getdist((xval[i],yval[i]),(xval[i+1],yval[i+1]))
            sum += self.getdist((xval[len(xval)-1],yval[len(xval)-1]),(xval[0],yval[0]))
            totalDis[j] = sum
        TotalDis = totalDis.copy()
        Dissort = sorted(TotalDis,reverse=True);m1 = Dissort[int(self.popsize/5)];m2 = Dissort[int(self.popsize/3)];m3 = Dissort[int(4*self.popsize/5)];m4 = Dissort[int(99*self.popsize/100)+1];m5 = Dissort[-3]
        m = [m1,m2,m3,m4,m5]
        Fitness = np.zeros_like(totalDis)
        count = 4
        for i in Fitness_choice[4::-1]:
            Fitness[TotalDis>m[count]] = i
            count -= 1
        Fitness[TotalDis<=m[4]] = Fitness_choice[5]
        return Fitness, totalDis

    def selection(self, fitness):
        idx = np.random.choice(np.arange(self.popsize), size=self.popsize, replace=True, p=fitness / fitness.sum())
        return self.pop[idx]

    def crossover(self, parent, pop):
        if np.random.rand() < self.pc:
            i = np.random.randint(0, self.popsize, size=1)  # 随机选取一个个体进行交换
            if np.random.rand() < 0.3:
                cross_points = np.random.randint(0, 2, self.citys).astype(np.bool)  # 随机