matlab【改进的多种群遗传算法】IMOMPGA【车间调度问题】

%% 清空环境 clc;clear close all %% 下载数据 load scheduleData Jm T JmNumber %工序 时间 tic %% 基本参数 NIND=50; %个体数目 MAXGEN=100; %最大遗传代数 MP=10; %种群数目 GGAP=0.7; %代沟 XOVR=0.6 + (0.9-0.6)*rand(MP,1); %交叉率0.7-0.9 MUTR=0.6 + (0.6-0.1)*rand(MP,1); %变异率0.1-0.6 gen=0; %代计数器 gen0 =0; %PNumber 工件个数 MNumber 工序个数 [PNumber, MNumber]=size(Jm); trace=zeros(2, MAXGEN); %寻优结果的初始值 trace_group={}; WNumber=PNumber*MNumber; %工序总个数 %% 初始化 Number=zeros(1,PNumber); % PNumber 工件个数 for i=1:PNumber Number(i)=MNumber; %MNumber工序个数 end for k=1:MP % 代码2层，第一层工序，第二层机器 Chrom=zeros(NIND,2*WNumber); for j=1:NIND WPNumberTemp=Number; for i=1:WNumber %随机产成工序 val=unidrnd(PNumber); while WPNumberTemp(val)==0 val=unidrnd(PNumber); end %第一层代码表示工序 Chrom(j,i)= val; WPNumberTemp(val)=WPNumberTemp(val)-1; %第2层代码表示机器 Temp=Jm{val,MNumber-WPNumberTemp(val)}; SizeTemp=length(Temp); %随机产成工序机器 Chrom(j,i+WNumber)= unidrnd(SizeTemp); end end Chrom_group{k}=Chrom; end %计算目标函数值 for k=1:MP [~, ObjV, ~, ~]=cal(Chrom_group{k},JmNumber,T,Jm); ObjV_group{k}=ObjV; end MinObjV=ones(MP,1) * 100000; %记录精华种群 [~,choromlen]=size(Chrom); MinChrom=zeros(MP,choromlen) ; %记录精华种群的编码 minY=10000; % 当代所有中种群的最佳 % % 初始化帕累托前沿和帕累托集合 % pareto_front = fitnesses(1,:); % pareto_set = cell(1,1); % pareto_set{1} = population{1}; %% 循环寻找 keep_gen=0; while gen<MAXGEN %代计数器增加 gen=gen+1; for i=1:length(Chrom_group) %分配适应度值 FitnV{i}=ranking(ObjV_group{i}); %选择操作 SelCh{i}=select('rws', Chrom_group{i}, FitnV{i}, GGAP); %交叉操作 SelCh{i}=across(SelCh{i},XOVR(i),Jm,T); %变异操作 SelCh{i}=aberranceJm(SelCh{i},MUTR(i),Jm,T); %计算目标适应度值 [PVal, ObjVSel, P, S]=cal(SelCh{i},JmNumber,T,Jm); %重新插入新种群 [Chrom_group{i}, ObjV_group{i}] =reins(Chrom_group{i}, SelCh{i},1, 1, ObjV_group{i}, ObjVSel); %保存最优值 trace(1, gen)=min(ObjV_group{i}); trace(2, gen)=mean(ObjV_group{i}); trace_group{i}=trace; end % 当达到移民周期 if mod(gen,4)==0 fprintf('当前代数%d',gen); [Chrom_group, ObjV_group] = migrant(Chrom_group, ObjV_group); % 移民 end % 当达到合并周期 if mod(gen,20)==0 fprintf('当前代数%d',gen); [Chrom_group, ObjV_group]=integrate(Chrom_group,ObjV_group); end [MinObjV,MinChrom]=EliteInduvidual(Chrom_group,ObjV_group,MinObjV,MinChrom); % 人工选择精华种群 [minval, index]=min(MinObjV); YY(gen)=minval;%最小时间 [PVal, ObjVSel, P, S]=cal(MinChrom(index,:),JmNumber,T,Jm); % 记录保持代数 if YY(gen) == minY keep_gen = keep_gen +1; end %记录 最小的工序 if YY(gen)<minY %判断当前优化值是否与前一次优化值相同 minY=YY(gen); %更新最优值 Val1=PVal; Val2=P;% 工序 STemp=S; %基因 end % 连续5代不变，提前停止 % if keep_gen>5 % break; % end end % 当前最佳值 PVal=Val1; %工序时间 P=Val2; %工序 S=STemp; %调度基因含机器基因 toc %% 描绘解的变化 figure(1) for i=1:MP-5 plot(trace_group{i}(1,:),'lineWidth',1.5); hold on; plot(trace_group{i}(2,:),'-.','lineWidth',1.5,'marker','^'); hold on end xlabel('Generations') ylabel('Change in optimal solution for each population') title(['Changes in optimal solutions for each population']) xlim([1,gen]) %% hold on plot(1:gen,YY,'lineWidth',1.5) legend('Change of solution of merging population1','Change of mean vlaue of merging population1','Change of solution of merging population2','Change of mean vlaue of merging population2','Change of solution of merging population3','Change of mean vlaue of merging population3',... 'Change of solution of merging population4','Change of mean vlaue of merging population4','Change of solution of merging population5','Change of mean vlaue of merging population5','MPGA change curve') xlabel('MPGA generations') ylabel('Change in optimal solution of MPGA') title('Evolutionary Process ') grid; xlim([1,gen]) %% 显示最优解 figure(3); MP=S(1,PNumber*MNumber+1:PNumber*MNumber*2); c_map = [0.57, 0.69, 0.30 0.89, 0.88, 0.57 0.76, 0.49, 0.58 0.47, 0.76, 0.81 0.21, 0.21, 0.35 0.28, 0.57, 0.54 0.07, 0.35, 0.40 0.41, 0.20, 0.42 0.60, 0.24, 0.18 0.76, 0.84, 0.65]; finish_time=zeros(1,6); for i=1:WNumber val= P(1,i); a=(mod(val,100)); %工序 b=((val-a)/100); %工件 Temp=Jm{b,a}; mText=Temp(MP(1,i)); x1=PVal(1,i); x2=PVal(2,i); y1=mText-1 + 0.15; y2=mText - 0.15; plotRec(x1,x2,mText -0.15); plotRec(PVal(1,i),PVal(2,i),mText-0.15); hold on; finish_time(b)=x2; % [1-1/b,1/b,b/PNumber] fill([x1,x2,x2,x1],[y1,y1,y2,y2],c_map(b)); text((x1+x2)/2,mText-0.25,num2str(P(i))); end xlabel('Time') ylabel('Machine') title('Gantt Chart') %% fuzzy_mat=[40, 55, 60, 70; 30, 40, 50, 55; 65, 80, 90, 110; 45, 50, 65, 75; 60, 70, 80, 95; 50, 55, 70, 80]; content = calcontent(finish_time,fuzzy_mat); for i=1:length(content) fprintf('工件%d的顾客满意度为%f\n',i,content(i)); end fprintf('最少时间%ds\n',YY(end)); %% GA