遗传算法实现公交车调度MATLAB

clear all; close all; clc; %变量初始化 populationSize=80; %群体大小 generationSize=800; %进化代数 codeLength=35; %编码长度 children=zeros(populationSize,7); %存储子代分量及适应度 pr=0.15; %复制概率 pm=0.1; %变异概率 pc=0.60; %交叉概率 E=round(rand(populationSize,codeLength)); %随机产生二进制串 %主函数 for k=1:1:generationSize for s=1:1:populationSize x=E(s,:); temp=zeros(1,7);%临时保存子代各分量的十进制值 %编码 for i=1:35 if i<=6 temp(1)=temp(1)+x(i)*2^(i-1); elseif i>=7 && i<=13 temp(2)=temp(2)+x(i)*2^(i-6-1); elseif i>=14 && i<=19 temp(3)=temp(3)+x(i)*2^(i-13-1); elseif i>=20 && i<=25 temp(4)=temp(4)+x(i)*2^(i-19-1); elseif i>=26 && i<=30 temp(5)=temp(5)+x(i)*2^(i-25-1); elseif i>=31 && i<=35 temp(6)=temp(6)+x(i)*2^(i-30-1); end end children(s,1)=round(60*temp(1)/(2^6-1)); children(s,2)=round(70*temp(2)/(2^7-1)); children(s,3)=round(60*temp(3)/(2^6-1)); children(s,4)=round(50*temp(4)/(2^6-1)); children(s,5)=round(20*temp(5)/(2^5-1)); children(s,6)=round(30*temp(6)/(2^5-1)); children(s,7)=round(children(s,1)+children(s,2)+children(s,3)+children(s,4)+children(s,5)+children(s,6)); while children(s,1)+children(s,6)<60||children(s,1)+children(s,2)<70||children(s,2)+children(s,3)<60||... children(s,3)+children(s,4)<50||children(s,4)+children(s,5)<20||children(s,5)+children(s,6)<30 E(s,:)=round(rand(1,codeLength)); x=E(s,:); temp=zeros(1,7); for i=1:35 if i<=6 temp(1)=temp(1)+x(i)*2^(i-1); elseif i>=7 && i<=13 temp(2)=temp(2)+x(i)*2^(i-6-1); elseif i>=14 && i<=19 temp(3)=temp(3)+x(i)*2^(i-13-1); elseif i>=20 && i<=25 temp(4)=temp(4)+x(i)*2^(i-19-1); elseif i>=26 && i<=30 temp(5)=temp(5)+x(i)*2^(i-25-1); elseif i>=31 && i<=35 temp(6)=temp(6)+x(i)*2^(i-30-1); end end children(s,1)=round(60*temp(1)/(2^6-1)); children(s,2)=round(70*temp(2)/(2^7-1)); children(s,3)=round(60*temp(3)/(2^6-1)); children(s,4)=round(50*temp(4)/(2^6-1)); children(s,5)=round(20*temp(5)/(2^5-1)); children(s,6)=round(30*temp(6)/(2^5-1)); children(s,7)=round(children(s,1)+children(s,2)+children(s,3)+children(s,4)+children(s,5)+children(s,6)); end end % Step 1 : 确定适应度函数 fitness=1./children(:,7); [orderFitness,indexFitness]=sort(fitness); %orderFitness按行从小到大排序的新矩阵,indexFitness元素位置构成的新矩阵 bestFitness=orderFitness(populationSize); %bestFitness=max(fitness) bestS=E(indexFitness(populationSize),:); %bestS 精英保留 %Step 2 : 复制操作 sumFitness=sum(fitness); fitnessSize=(orderFitness/sumFitness)*populationSize; fitnessSelect=floor(fitnessSize); %朝负无穷大方向取整 kk=1; for i=1:1:populationSize for j=1:1:fitnessSelect(i) %Select and Reproduce TempE(kk,:)=E(indexFitness(i),:); kk=kk+1; %kk is used to reproduce end end %Step 3 : 交叉操作 n=ceil(35*rand); %朝正无穷大方向取整 for i=1:2:(populationSize-1) temp=rand; if pc>temp %Crossover Condition for j=n:1:35 TempE(i,j)=E(i+1,j); TempE(i+1,j)=E(i,j); end end end TempE(populationSize,:)=bestS; E=TempE; %Step 4: 变异操作 for i=1:1:populationSize for j=1:1:35 temp=rand; if pm>temp %Mutation Condition if TempE(i,j)==0 TempE(i,j)=1; else TempE(i,j)=0; end end end end TempE(populationSize,:)=bestS; E=TempE; disp(['第',num2str(k),'代:',num2str(children(populationSize,:))]); end disp(['二进制编码:',num2str(bestS)]); disp(['最优选择：',num2str(children(populationSize,:))]);