200字范文 > 【优化求解】基于matlab遗传算法求解道路流量优化问题【含Matlab源码 1480期】

【优化求解】基于matlab遗传算法求解道路流量优化问题【含Matlab源码 1480期】

时间：2018-10-21 12:41:48

一、获取代码方式

获取代码方式1：

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获取代码方式2：

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二、遗传算法简介

1 引言

2 遗传算法理论

2.1 遗传算法的生物学基础

2.2 遗传算法的理论基础

2.3 遗传算法的基本概念

2.4 标准的遗传算法

2.5 遗传算法的特点

2.6 遗传算法的改进方向

3 遗传算法流程

4 关键参数说明

三、部分源代码

%% 清空环境clearclcglobal W Cmax Mxf1 Mxf2 Mxf3 Mxf4 Q0 b1 b2 b3 b4 N%% 已知参数W=[9 14 5 9 8 7 8 7 6 10 10 9 8 6 5 10 8 9 11]; %零流时间Cmax=[700 900 800 600 600 900 600 300 900 500 700 600 700 500 400 800 700 700 600]; %路段能力%路段-路径矩阵Mxf1=[1,1,1,1,1,1,00,0,0,0,0,0,11,1,1,0,0,0,00,0,0,1,1,1,00,0,0,0,0,0,00,0,0,0,0,0,00,0,0,0,0,0,00,0,0,0,0,0,01,0,0,0,0,0,00,1,1,0,0,0,00,0,0,1,1,0,00,0,0,0,0,1,10,1,0,1,0,0,00,0,1,0,1,0,01,0,0,0,0,0,00,0,0,0,0,0,01,1,0,1,0,0,00,0,0,0,0,0,00,0,1,0,1,1,1];Mxf2=[1,1,1,1,00,0,0,0,11,1,1,0,00,0,0,1,00,0,0,0,00,0,0,0,00,0,0,0,00,0,0,0,01,1,0,0,00,0,1,0,00,0,0,1,10,0,0,0,00,0,1,1,10,0,0,0,00,1,0,0,01,0,0,0,00,0,0,0,00,1,1,1,10,0,0,0,0];Mxf3=[0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,00,0,0,0,1,1,1,0,0,01,1,1,0,0,0,0,0,0,00,0,0,1,0,0,0,1,1,11,1,1,0,1,1,1,0,0,00,0,0,0,0,0,0,1,1,10,0,0,1,0,0,0,0,0,00,0,0,0,0,0,1,0,0,10,0,0,0,1,1,0,1,1,00,1,1,0,0,0,0,0,0,01,0,0,0,0,0,0,0,0,00,0,1,0,0,1,0,0,1,00,1,0,0,1,0,0,1,0,00,0,0,1,0,0,1,0,0,10,0,0,0,0,0,0,0,0,00,0,1,1,0,1,1,0,1,10,0,0,0,0,0,0,0,0,01,1,0,0,1,0,0,1,0,0];Mxf4=[0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,00,0,0,0,0,1,1,1,00,0,0,0,0,0,0,0,11,1,1,1,1,0,0,0,00,0,0,0,0,1,1,1,10,0,1,1,1,0,0,0,01,1,0,0,0,0,0,0,00,0,1,1,0,1,1,0,00,0,0,0,1,0,0,1,00,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,00,0,0,0,1,0,0,1,10,0,0,0,0,0,0,0,00,1,0,1,0,0,1,0,01,0,1,0,0,1,0,0,00,0,0,0,0,0,0,0,00,1,0,1,1,0,1,1,10,0,0,0,0,0,0,0,0]; b1=length(Mxf1(1,:));b2=length(Mxf2(1,:));b3=length(Mxf3(1,:));b4=length(Mxf4(1,:));%% OD对需求Q0=[300 500 700 300];%% 设置遗传参数MAXGEN=500; %最大迭代代数 popsize=500; %设置初始参数，种群大小pc=0.8; %设置交叉概率pm=0.1; %设置变异概率N=b1+b2+b3+b4-4; %编码长度Chrom=zeros(popsize,N);%创建初始种群for i=1:popsizeChrom(i,:)=crtzq(N); endObjV=zeros(popsize,1);for i=1:popsizeObjV(i)=Objfun(Chrom(i,:)); end[objv,f]=sort(ObjV);bjv(1:popsize-1)=objv(1:popsize-1);Chrom=Chrom(f,:);chrom=Chrom(1:popsize-1,:);fsum=sum(bjv);Pperpopulation=bjv/fsum;%计算累积概率cumsump(1)=Pperpopulation(1);for i=2:popsize-1cumsump(i)=cumsump(i-1)+Pperpopulation(i);endcumsump=cumsump';Generation=1;y=zeros(MAXGEN,2);while Generation<MAXGEN+1for j=1:2:popsize-1%选择操作seln=selection(cumsump);scro=crossover(chrom,seln,pc);scnew(j,:)=scro(1,:);scnew(j+1,:)=scro(2,:);%变异操作smnew(j,:)=mutation(scnew(j,:),pm);smnew(j+1,:)=mutation(scnew(j+1,:),pm);endchrom=smnew; %产生了新的种群Chrom(1:popsize-1,:)=chrom(1:popsize-1,:);for i=1:1:size(Chrom,1)[ObjV(i)]=Objfun(Chrom(i,:));end[objv,f]=sort(ObjV);bjv(1:popsize-1)=objv(1:popsize-1);Chrom=Chrom(f,:);y(Generation,1)=1e7/Objfun(Chrom(end,:));y(Generation,2)=mean(1e7./ObjV);chrom=Chrom(1:popsize-1,:);fsum=sum(bjv);Pperpopulation=bjv/fsum;%计算累积概率cumsump(1)=Pperpopulation(1);for i=2:popsize-1cumsump(i)=cumsump(i-1)+Pperpopulation(i);endGeneration=Generation+1end%子程序：判断遗传运算是否需要进行交叉或变异, 函数名称存储为IfCroIfMut.mfunction pcc=IfCroIfMut(mutORcro)test(1:100)=0;l=round(100*mutORcro);test(1:l)=1;n=round(rand*99)+1;pcc=test(n);%子程序：新种群交叉操作,函数名称存储为crossover.mfunction scro=crossover(Chrom,seln,pc)global Npcc=IfCroIfMut(pc);if pcc==1chb=randperm(N-1,1);scro(1,:)=[Chrom(seln(1),1:chb) Chrom(seln(2),chb+1:N)];scro(2,:)=[Chrom(seln(2),1:chb) Chrom(seln(1),chb+1:N)]; elsescro(1,:)=Chrom(seln(1),:);scro(2,:)=Chrom(seln(2),:);end