📄 车间作业调度问题遗传算法matlab源码.htm
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<td><p align="center"><h4>车间作业调度问题遗传算法Matlab源码</h4></p>
<p align="center">作者：佚名 来源：网络 时间：2007-3-28 12:51:33 阅读次数：498</p>
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</div><br><br><P><FONT face="Times New Roman">function [Zp,Y1p,Y2p,Y3p,Xp,LC1,LC2]=JSPGA(M,N,Pm,T,P)<BR>%--------------------------------------------------------------------------<BR>% JSPGA.m<BR>% 车间作业调度问题遗传算法<BR>%--------------------------------------------------------------------------<BR>% 输入参数列表<BR>% M 遗传进化迭代次数<BR>% N 种群规模(取偶数)<BR>% Pm 变异概率<BR>% T m×n的矩阵，存储m个工件n个工序的加工时间<BR>% P 1×n的向量，n个工序中，每一个工序所具有的机床数目<BR>% 输出参数列表<BR>% Zp 最优的Makespan值<BR>% Y1p 最优方案中，各工件各工序的开始时刻，可根据它绘出甘特图<BR>% Y2p 最优方案中，各工件各工序的结束时刻，可根据它绘出甘特图<BR>% Y3p 最优方案中，各工件各工序使用的机器编号<BR>% Xp 最优决策变量的值，决策变量是一个实数编码的m×n矩阵<BR>% LC1 收敛曲线1，各代最优个体适应值的记录<BR>% LC2 收敛曲线2，各代群体平均适应值的记录<BR>% 最后，程序还将绘出三副图片：两条收敛曲线图和甘特图（各工件的调度时序图）</FONT></P>
<P><FONT face="Times New Roman">%第一步：变量初始化<BR>[m,n]=size(T);%m是总工件数，n是总工序数<BR>Xp=zeros(m,n);%最优决策变量<BR>LC1=zeros(1,M);%收敛曲线1<BR>LC2=zeros(1,N);%收敛曲线2</FONT></P>
<P><FONT face="Times New Roman">%第二步：随机产生初始种群<BR>farm=cell(1,N);%采用细胞结构存储种群<BR>for k=1:N<BR> X=zeros(m,n);<BR> for j=1:n<BR> for i=1:m<BR> X(i,j)=1+(P(j)-eps)*rand;<BR> end<BR> end<BR> farm{k}=X;<BR>end</FONT></P>
<P><FONT face="Times New Roman">counter=0;%设置迭代计数器<BR>while counter<BR> <BR> %第三步：交叉<BR> newfarm=cell(1,N);%交叉产生的新种群存在其中<BR> Ser=randperm(N);<BR> for i=1:2:(N-1)<BR> A=farm{Ser(i)};%父代个体<BR> B=farm{Ser(i+1)};<BR> Manner=unidrnd(2);%随机选择交叉方式<BR> if Manner==1<BR> cp=unidrnd(m-1);%随机选择交叉点<BR> %双亲双子单点交叉<BR> a=[A(1:cp,:);B((cp+1):m,:)];%子代个体<BR> b=[B(1:cp,:);A((cp+1):m,:)];<BR> else<BR> cp=unidrnd(n-1);%随机选择交叉点<BR> a=[A(:,1:cp),B(:,(cp+1):n)];%双亲双子单点交叉<BR> b=[B(:,1:cp),A(:,(cp+1):n)];<BR> end<BR> newfarm{i}=a;%交叉后的子代存入newfarm<BR> newfarm{i+1}=b;<BR> end<BR> %新旧种群合并<BR> FARM=[farm,newfarm];<BR> <BR> %第四步：选择复制<BR> FITNESS=zeros(1,2*N);<BR> fitness=zeros(1,N);<BR> plotif=0;<BR> for i=1:(2*N)<BR> X=FARM{i};<BR> Z=COST(X,T,P,plotif);%调用计算费用的子函数<BR> FITNESS(i)=Z;<BR> end<BR> %选择复制采取两两随机配对竞争的方式，具有保留最优个体的能力<BR> Ser=randperm(2*N);<BR> for i=1:N<BR> f1=FITNESS(Ser(2*i-1));<BR> f2=FITNESS(Ser(2*i));<BR> if f1<=f2<BR> farm{i}=FARM{Ser(2*i-1)};<BR> fitness(i)=FITNESS(Ser(2*i-1));<BR> else<BR> farm{i}=FARM{Ser(2*i)};<BR> fitness(i)=FITNESS(Ser(2*i));<BR> end<BR> end<BR> %记录最佳个体和收敛曲线<BR> minfitness=min(fitness)<BR> meanfitness=mean(fitness)<BR> LC1(counter+1)=minfitness;%收敛曲线1，各代最优个体适应值的记录<BR> LC2(counter+1)=meanfitness;%收敛曲线2，各代群体平均适应值的记录<BR> pos=find(fitness==minfitness);<BR> Xp=farm{pos(1)};<BR> <BR> %第五步：变异<BR> for i=1:N<BR> if Pm>rand;%变异概率为Pm<BR> X=farm{i};<BR> I=unidrnd(m);<BR> J=unidrnd(n);<BR> X(I,J)=1+(P(J)-eps)*rand;<BR> farm{i}=X;<BR> end<BR> end<BR> farm{pos(1)}=Xp;<BR> <BR> counter=counter+1<BR>end</FONT></P>
<P><FONT face="Times New Roman">%输出结果并绘图<BR>figure(1);<BR>plotif=1;<BR>X=Xp;<BR>[Zp,Y1p,Y2p,Y3p]=COST(X,T,P,plotif);<BR>figure(2);<BR>plot(LC1);<BR>figure(3);<BR>plot(LC2);</FONT><FONT face="Times New Roman"> </FONT></P><br>
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