改进遗传算法在TSP问题中的应用（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

🌈3 Matlab代码实现

🎉4 参考文献

💥1 概述

物流业作为国家经济体系的重要组成部分,费用居高不下,特别是运输费用达到物流费用的一半以上,是导致物流成本过高的重要因素。旅行商问题是车辆路径问题的特例,是实现车辆路径优化的关键。

📚2 运行结果

部分代码：

clc
clear
close all

Fixedcosts = 150; %固定成本
nuitTransCost =2.4; %单位运输成本
coldRate=1.3; %制冷率
congesteRate=1.5; %拥堵率
goodLossRate = 0.39; %货损率
openDoorCost = 30; %一次开门费用
openDoorCostRate = 0.26; %开门费率
MaxDistributeRidus = 15; %最大费送半径
veichleSpeed = 60; %车辆速度
veichleMaxW = 200; %车辆最大装载量
Popsize=500; %染色体数量
Iteration=100; %迭代次数
Pc=0.75; %交叉率 0-1之间
Pm=0.7; %变异率 0-1之间
step = 0; % 初始化阶段
start = [40 50]; %中心点坐标
pos =load('坐标.txt');
demandArr = load('需求量.txt');
timeWindows = load('时间窗.txt');
server = load('卸货时间.txt');
posInfo = [];
[totalIn,disTribtePoint] = gaMain(Fixedcosts,nuitTransCost,coldRate,congesteRate,goodLossRate,start,openDoorCost,openDoorCostRate,MaxDistributeRidus,veichleSpeed,veichleMaxW,Popsize,Iteration,Pc,Pm,pos,demandArr,timeWindows,server,step,posInfo);
[totalIn,~]=sortrows(totalIn,3);
op = 1;
while totalIn(op,3)<0
totalIn(op,3) = totalIn(op,3)*(-1);
op = op +1;
end
[totalIn,I]=sortrows(totalIn,3);
inPutNew = totalIn;
distributePos = [];
for n=1:max(inPutNew(:,3))
distributePos = [distributePos ;inPutNew(find(inPutNew(:,3) == n,1),:)];
end
save('distributePos','distributePos');
[inSize,ii] = size(inPutNew);
jPos = 1;
save('jPos','jPos');
save('inSize','inSize');
save('inPutNew','inPutNew');
% for i =1:disTribtePoint -1
for i =1:disTribtePoint
save('i','i');save('disTribtePoint','disTribtePoint');save('jPos','jPos');
clear;
load('jPos','jPos');load('inSize','inSize');load('inPutNew','inPutNew');load('i','i');load('disTribtePoint','disTribtePoint');
step = i;
ho = 0;
for j = jPos:inSize
if inPutNew(j,3) == i
ho = ho +1;
end
end
jPos = jPos + ho;
realIn = inPutNew(jPos-ho:(jPos-1),:);
Fixedcosts = 80; %固定成本
nuitTransCost =1; %单位运输成本
coldRate=0; %制冷率
congesteRate=0; %拥堵率
goodLossRate = 0.5; %货损率
openDoorCost = 0; %一次开门费用
openDoorCostRate = 0; %开门费率
MaxDistributeRidus = 9999; %最大配送半径
veichleSpeed = 25; %车辆速度
veichleMaxW = 70; %车辆最大装载量
Popsize=100; %染色体数量
Iteration=100; %迭代次数
Pc=0.75; %交叉率 0-1之间
Pm=0.7; %变异率 0-1之间
demandArr = load('需求量.txt');
timeWindows = load('时间窗.txt');
server = load('卸货时间.txt');
[demandArr,timeWindows,server] = dealExat(demandArr,timeWindows,server,realIn);
[realInSize,~] = size(realIn);
oneRowp = [1:realInSize-1]';
realInp = [realIn(2:end,:) oneRowp];
posInfo = realInp(:,4:5);

pos =realIn(:,1:2);
start = pos(1, :);
pos(1,:) = [];
gaMain(Fixedcosts,nuitTransCost,coldRate,congesteRate,goodLossRate,start,openDoorCost,openDoorCostRate,MaxDistributeRidus,veichleSpeed,veichleMaxW,Popsize,Iteration,Pc,Pm,pos,demandArr,timeWindows,server,step,posInfo);
end