【TSP问题】基于帝企鹅算法求解旅行商问题附matlab代码

【TSP问题】基于帝企鹅算法求解旅行商问题附matlab代码

1 简介

2 部分代码

%% 数量固定% TSP issues with different starting points and a fixed number of travellers%% 这是使用原始算法的直接求解结果，添加专用于本问题的更新方式可以进一步提高精度% This is the direct result of using the original algorithm, % adding some specific update methods to this problem can further improve the accuracyclc;clear;close all;warning off%% 固定随机数种子noRNG=1;rng('default')rng(noRNG)%% 载入数据data.maxTraveler=3; %旅行商数量data.numCity=30; %城市数量%% 随机生成城市data.xyCity=rand(data.numCity,2);for i=1:data.numCity for j=1:data.numCity data.D(i,j)=norm(data.xyCity(i,:)-data.xyCity(j,:)); endend%%option.dim=data.numCity;lb=0;ub=1;option.lb=lb;option.ub=ub;if length(option.lb)==1 option.lb=ones(1,option.dim)*option.lb; option.ub=ones(1,option.dim)*option.ub;endoption.fobj=@aimFcn_1;%option.fobj0=option.fobj;option.showIter=0;%% 算法参数设置 Parameters% 基本参数option.numAgent=200; %种群个体数 size of populationoption.maxIteration=100; %最大迭代次数 maximum number of interation% 帝企鹅算法option.v_lb=-(option.ub-option.lb)/4;option.v_ub=(option.ub-option.lb)/4;option.w2=0.5; %weight of Moving strategy IIIoption.w4=1;%weight of Moving strategy IIIoption.w5=1;%weight of Moving strategy IIIoption.pe=0.01; % rate to judge Premature convergenceoption.gap0=ceil(sqrt(option.maxIteration*2))+1;option.gapMin=5; % min gapoption.dec=2; % dec of gapoption.L=10; % Catastrophestr_legend=[{'AFO1'},{'AFO2'}];%% Initialize population individuals (common to control experiment algorithm)x=ones(option.numAgent,option.dim);y=ones(option.numAgent,1);for i=1:option.numAgent x(i,:)=rand(size(option.lb)).*(option.ub-option.lb)+option.lb; y(i)=option.fobj(x(i,:),option,data);end%% 使用算法求解% Based on the same population, solve the selected functions by using different algorithmsbestX=x;rng(noRNG)tic[bestY(1,:),bestX(1,:),recording(1)]=AFO1(x,y,option,data);tt(1,1)=toc;rng(noRNG)tic[bestY(2,:),bestX(2,:),recording(2)]=AFO2(x,y,option,data);tt(1,2)=toc;%%figurehold onfor i=1:length(recording) plot((recording(i).bestFit),'LineWidth',2)endlegend(str_legend)title('fitness curve')%% 输出结果str='AFO1'[~,result1]=option.fobj(bestX(1,:),option,data);drawPc(result1,option,data,str)str='AFO2'[~,result2]=option.fobj(bestX(2,:),option,data);drawPc(result2,option,data,str)

3 仿真结果

4 参考文献

[1]李旭飞. 几类优化问题的帝企鹅优化算法研究[D]. 北方民族大学.

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