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免疫算法在物流中心选址问题中的matlab实现
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%% 免疫优化算法在物流配送中心选址中的应用%% 清空环境clcclear
%% 算法基本参数 sizepop=50; % 种群规模overbest=10; % 记忆库容量MAXGEN=100; % 迭代次数pcross=0.5; % 交叉概率pmutation=0.4; % 变异概率ps=0.95; % 多样性评价参数length=6; % 配送中心数M=sizepop+overbest;
%% step1 识别抗原,将种群信息定义为一个结构体individuals = struct('fitness',zeros(1,M), 'concentration',zeros(1,M),'excellence',zeros(1,M),'chrom',[]);%% step2 产生初始抗体群individuals.chrom = popinit(M,length);trace=[]; %记录每代最个体优适应度和平均适应度
%% 迭代寻优for iii=1:MAXGEN
%% step3 抗体群多样性评价 for i=1:M individuals.fitness(i) = fitness(individuals.chrom(i,:)); % 抗体与抗原亲和度(适应度值）计算 individuals.concentration(i) = concentration(i,M,individuals); % 抗体浓度计算 end % 综合亲和度和浓度评价抗体优秀程度，得出繁殖概率 individuals.excellence = excellence(individuals,M,ps); % 记录当代最佳个体和种群平均适应度 [best,index] = min(individuals.fitness); % 找出最优适应度 bestchrom = individuals.chrom(index,:); % 找出最优个体 average = mean(individuals.fitness); % 计算平均适应度 trace = [trace;best,average]; % 记录 %% step4 根据excellence，形成父代群，更新记忆库（加入精英保留策略，可由s控制） bestindividuals = bestselect(individuals,M,overbest); % 更新记忆库 individuals = bestselect(individuals,M,sizepop); % 形成父代群
%% step5 选择，交叉，变异操作，再加入记忆库中抗体，产生新种群 individuals = select(individuals,sizepop); % 选择 individuals.chrom = Cross(pcross,individuals.chrom,sizepop,length); % 交叉 individuals.chrom = mutation(pmutation,individuals.chrom,sizepop,length); % 变异 individuals = incorporate(individuals,sizepop,bestindividuals,overbest); % 加入记忆库中抗体
end
%% 画出免疫算法收敛曲线figure(1)plot(trace(:,1));hold onplot(trace(:,2),'--');legend('最优适应度值','平均适应度值')title('免疫算法收敛曲线','fontsize',12)xlabel('迭代次数','fontsize',12)ylabel('适应度值','fontsize',12)
%% 画出配送中心选址图%城市坐标city_coordinate=[1304,2312;3639,1315;4177,2244;3712,1399;3488,1535;3326,1556;3238,1229;4196,1044;4312,790;4386,570; 3007,1970;2562,1756;2788,1491;2381,1676;1332,695;3715,1678;3918,2179;4061,2370;3780,2212;3676,2578; 4029,2838;4263,2931;3429,1908;3507,2376;3394,2643;3439,3201;2935,3240;3140,3550;2545,2357;2778,2826;2370,2975];carge=[20,90,90,60,70,70,40,90,90,70,60,40,40,40,20,80,90,70,100,50,50,50,80,70,80,40,40,60,70,50,30];%找出最近配送点for i=1:31 distance(i,:)=dist(city_coordinate(i,:),city_coordinate(bestchrom,:)');end[a,b]=min(distance');
index=cell(1,length);
for i=1:length%计算各个派送点的地址index{i}=find(b==i);endfigure(2)title('最优规划派送路线')cargox=city_coordinate(bestchrom,1);cargoy=city_coordinate(bestchrom,2);plot(cargox,cargoy,'rs','LineWidth',2,... 'MarkerEdgeColor','r',... 'MarkerFaceColor','b',... 'MarkerSize',20)hold on
plot(city_coordinate(:,1),city_coordinate(:,2),'o','LineWidth',2,... 'MarkerEdgeColor','k',... 'MarkerFaceColor','g',... 'MarkerSize',10)
for i=1:31 x=[city_coordinate(i,1),city_coordinate(bestchrom(b(i)),1)]; y=[city_coordinate(i,2),city_coordinate(bestchrom(b(i)),2)]; plot(x,y,'c');hold onend1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798function fit=fitness(individual)%计算个体适应度%individual input 个体%fit output 适应度值%城市坐标city_coordinate=[1304,2312;3639,1315;4177,2244;3712,1399;3488,1535;3326,1556; 3238,1229;4196,1044;4312,790;4386,570;3007,1970;2562,1756; 2788,1491;2381,1676;1332,695;3715,1678;3918,2179;4061,2370; 3780,2212;3676,2578;4029,2838;4263,2931;3429,1908;3507,2376; 3394,2643;3439,3201;2935,3240;3140,3550;2545,2357;2778,2826; 2370,2975];%货物量carge=[20,90,90,60,70,70,40,90,90,70,60,40,40,40,20,80,90,70,100,50,50,50,80,70,80,40,40,60,70,50,30];%找出最近配送点for i=1:31 %dist函数就是欧式距离加权函数 distance(i,:)=dist(city_coordinate(i,:),city_coordinate(individual,:)');end[a,b]=min(distance');%计算费用for i=1:31 expense(i)=carge(i)*a(i);endfit=sum(expense)+4.0e+4*length(find(a>3000));
end1234567891011121314151617181920212223242526function resemble=similar(individual1,individual2)%计算个体individual1与individual2的相似度%individual1,individual2 input 两个个体%resemble output 相似度k=zeros(1,length(individual1));for i=1:length(individual1) if find(individual1(i)==individual2) k(i)=1; endendresemble=sum(k)/length(individual1);end123456789101112function concentration = concentration(i,M,individuals)% 计算个体浓度值% i input 第i个抗体% M input 种群规模% individuals input 个体% concentration output 浓度值
concentration=0;for j=1:M xsd=similar(individuals.chrom(i,:),individuals.chrom(j,:)); % 第i个体与种群个体间的相似度 % 相似度大于阀值 if xsd>0.7 concentration=concentration+1; endend
concentration=concentration/M;
end12345678910111213141516171819function exc = excellence(individuals,M,ps)%计算个体繁殖概率%individuals input 种群%M input 种群规模%ps input 多样性评价参数%exc output 繁殖概率fit = 1./individuals.fitness;sumfit = sum(fit);con = individuals.concentration;sumcon = sum(con);for i=1:M exc(i)=fit(i)/sumfit*ps+con(i)/sumcon*(1-ps);end
end1234567891011121314151617function ret=Select(individuals,sizepop)% 轮盘赌选择% individuals input : 种群信息% sizepop input : 种群规模% ret output : 选择后得到的种群
excellence=individuals.excellence;pselect=excellence./sum(excellence);% 事实上 pselect = excellence；
index=[]; for i=1:sizepop % 转sizepop次轮盘 pick=rand; while pick==0 pick=rand; end for j=1:sizepop pick=pick-pselect(j); if pick<0 index=[index j]; break; % 寻找落入的区间，此次转轮盘选中了染色体j end endend% 注意：在转sizepop次轮盘的过程中，有可能会重复选择某些染色体
individuals.chrom=individuals.chrom(index,:);individuals.fitness=individuals.fitness(index);individuals.concentration=individuals.concentration(index);individuals.excellence=individuals.excellence(index);ret=individuals;
end123456789101112131415161718192021222324252627282930313233function ret=Mutation(pmutation,chrom,sizepop,length1)% 变异操作% pmutation input : 变异概率% chrom input : 抗体群% sizepop input : 种群规模% iii input : 进化代数% MAXGEN input : 最大进化代数% length1 input : 抗体长度% ret output : 变异得到的抗体群% 每一轮for循环中，可能会进行一次变异操作，染色体是随机选择的，变异位置也是随机选择的for i=1:sizepop % 变异概率 pick=rand; while pick==0 pick=rand; end index=unidrnd(sizepop);
% 判断是否变异 if pick>pmutation continue; end pos=unidrnd(length1); while pos==1 pos=unidrnd(length1); end nchrom=chrom(index,:); nchrom(pos)=unidrnd(31); while length(unique(nchrom))==(length1-1) nchrom(pos)=unidrnd(31); end flag=test(nchrom); if flag==1 chrom(index,:)=nchrom; end end
ret=chrom;end1234567891011121314151617181920212223242526272829303132333435363738394041424344function newindividuals = incorporate(individuals,sizepop,bestindividuals,overbest)% 将记忆库中抗体加入，形成新种群% individuals input 抗体群% sizepop input 抗体数% bestindividuals input 记忆库% overbest input 记忆库容量
m = sizepop+overbest;newindividuals = struct('fitness',zeros(1,m), 'concentration',zeros(1,m),'excellence',zeros(1,m),'chrom',[]);
% 遗传操作得到的抗体for i=1:sizepop newindividuals.fitness(i) = individuals.fitness(i); newindividuals.concentration(i) = individuals.concentration(i); newindividuals.excellence(i) = individuals.excellence(i); newindividuals.chrom(i,:) = individuals.chrom(i,:); end% 记忆库中抗体for i=sizepop+1:m newindividuals.fitness(i) = bestindividuals.fitness(i-sizepop); newindividuals.concentration(i) = bestindividuals.concentration(i-sizepop); newindividuals.excellence(i) = bestindividuals.excellence(i-sizepop); newindividuals.chrom(i,:) = bestindividuals.chrom(i-sizepop,:); end
end1234567891011121314151617181920212223242526function psd=popinit(M,length) ss=[];for i=1:M a=randperm(31,length); ss(i,:)=a;endpsd=ss;
end123456789function rets=bestselect(individuals,m,n)% 初始化记忆库,依据excellence，将群体中高适应度低相似度的overbest个个体存入记忆库% m input 抗体数% n input 记忆库个体数\父代群规模% individuals input 抗体群% bestindividuals output 记忆库\父代群
% 精英保留策略，将fitness最好的s个个体先存起来，避免因其浓度高而被淘汰s=3;rets=struct('fitness',zeros(1,n), 'concentration',zeros(1,n),'excellence',zeros(1,n),'chrom',[]);[fitness,index] = sort(individuals.fitness);for i=1:s rets.fitness(i) = individuals.fitness(index(i)); rets.concentration(i) = individuals.concentration(index(i)); rets.excellence(i) = individuals.excellence(index(i)); rets.chrom(i,:) = individuals.chrom(index(i),:);end
% 剩余m-s个个体leftindividuals=struct('fitness',zeros(1,m-s), 'concentration',zeros(1,m-s),'excellence',zeros(1,m-s),'chrom',[]);for k=1:m-s leftindividuals.fitness(k) = individuals.fitness(index(k+s)); leftindividuals.concentration(k) = individuals.concentration(index(k+s)); leftindividuals.excellence(k) = individuals.excellence(index(k+s)); leftindividuals.chrom(k,:) = individuals.chrom(index(k+s),:);end
% 将剩余抗体按excellence值排序[excellence,index]=sort(1./leftindividuals.excellence);
% 在剩余抗体群中按excellence再选n-s个最好的个体for i=s+1:n rets.fitness(i) = leftindividuals.fitness(index(i-s)); rets.concentration(i) = leftindividuals.concentration(index(i-s)); rets.excellence(i) = leftindividuals.excellence(index(i-s)); rets.chrom(i,:) = leftindividuals.chrom(index(i-s),:);end
end123456789101112131415161718192021222324252627282930313233343536373839function ret=Cross(pcross,chrom,sizepop,length)% 交叉操作% pcorss input : 交叉概率% chrom input : 抗体群% sizepop input : 种群规模% length input : 抗体长度% ret output : 交叉得到的抗体群
% 每一轮for循环中，可能会进行一次交叉操作，随机选择染色体是和交叉位置，是否进行交叉操作则由交叉概率（continue）控制for i=1:sizepop % 随机选择两个染色体进行交叉 pick=rand; while prod(pick)==0 pick=rand(1); end if pick>pcross continue; end % 找出交叉个体 index(1)=unidrnd(sizepop); index(2)=unidrnd(sizepop); while index(2)==index(1) index(2)=unidrnd(sizepop); end % 选择交叉位置 pos=ceil(length*rand); while pos==1 pos=ceil(length*rand); end
% 个体交叉 chrom1=chrom(index(1),:); chrom2=chrom(index(2),:); k=chrom1(pos:length); chrom1(pos:length)=chrom2(pos:length); chrom2(pos:length)=k; % 满足约束条件赋予新种群 flag1=test(chrom(index(1),:)); flag2=test(chrom(index(2),:)); if flag1*flag2==1 chrom(index(1),:)=chrom1; chrom(index(2),:)=chrom2; end end
ret=chrom;end12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455function flag=test(code)% 检查个体是否满足距离约束% code input 个体% flag output 是否满足要求标志
city_coordinate=[1304,2312;3639,1315;4177,2244;3712,1399;3488,1535;3326,1556;3238,1229;4196,1044;4312,790;4386,570; 3007,1970;2562,1756;2788,1491;2381,1676;1332,695;3715,1678;3918,2179;4061,2370;3780,2212;3676,2578; 4029,2838;4263,2931;3429,1908;3507,2376;3394,2643;3439,3201;2935,3240;3140,3550;2545,2357;2778,2826;2370,2975];
flag=1;if max( max(dist( city_coordinate(code,:)') ) )>3000 flag=0;end
end
%% 算法基本参数 sizepop=50; % 种群规模overbest=10; % 记忆库容量MAXGEN=100; % 迭代次数pcross=0.5; % 交叉概率pmutation=0.4; % 变异概率ps=0.95; % 多样性评价参数length=6; % 配送中心数M=sizepop+overbest;
%% step1 识别抗原,将种群信息定义为一个结构体individuals = struct('fitness',zeros(1,M), 'concentration',zeros(1,M),'excellence',zeros(1,M),'chrom',[]);%% step2 产生初始抗体群individuals.chrom = popinit(M,length);trace=[]; %记录每代最个体优适应度和平均适应度
%% 迭代寻优for iii=1:MAXGEN
%% step3 抗体群多样性评价 for i=1:M individuals.fitness(i) = fitness(individuals.chrom(i,:)); % 抗体与抗原亲和度(适应度值）计算 individuals.concentration(i) = concentration(i,M,individuals); % 抗体浓度计算 end % 综合亲和度和浓度评价抗体优秀程度，得出繁殖概率 individuals.excellence = excellence(individuals,M,ps); % 记录当代最佳个体和种群平均适应度 [best,index] = min(individuals.fitness); % 找出最优适应度 bestchrom = individuals.chrom(index,:); % 找出最优个体 average = mean(individuals.fitness); % 计算平均适应度 trace = [trace;best,average]; % 记录 %% step4 根据excellence，形成父代群，更新记忆库（加入精英保留策略，可由s控制） bestindividuals = bestselect(individuals,M,overbest); % 更新记忆库 individuals = bestselect(individuals,M,sizepop); % 形成父代群
%% step5 选择，交叉，变异操作，再加入记忆库中抗体，产生新种群 individuals = select(individuals,sizepop); % 选择 individuals.chrom = Cross(pcross,individuals.chrom,sizepop,length); % 交叉 individuals.chrom = mutation(pmutation,individuals.chrom,sizepop,length); % 变异 individuals = incorporate(individuals,sizepop,bestindividuals,overbest); % 加入记忆库中抗体
end
%% 画出免疫算法收敛曲线figure(1)plot(trace(:,1));hold onplot(trace(:,2),'--');legend('最优适应度值','平均适应度值')title('免疫算法收敛曲线','fontsize',12)xlabel('迭代次数','fontsize',12)ylabel('适应度值','fontsize',12)
%% 画出配送中心选址图%城市坐标city_coordinate=[1304,2312;3639,1315;4177,2244;3712,1399;3488,1535;3326,1556;3238,1229;4196,1044;4312,790;4386,570; 3007,1970;2562,1756;2788,1491;2381,1676;1332,695;3715,1678;3918,2179;4061,2370;3780,2212;3676,2578; 4029,2838;4263,2931;3429,1908;3507,2376;3394,2643;3439,3201;2935,3240;3140,3550;2545,2357;2778,2826;2370,2975];carge=[20,90,90,60,70,70,40,90,90,70,60,40,40,40,20,80,90,70,100,50,50,50,80,70,80,40,40,60,70,50,30];%找出最近配送点for i=1:31 distance(i,:)=dist(city_coordinate(i,:),city_coordinate(bestchrom,:)');end[a,b]=min(distance');
index=cell(1,length);
for i=1:length%计算各个派送点的地址index{i}=find(b==i);endfigure(2)title('最优规划派送路线')cargox=city_coordinate(bestchrom,1);cargoy=city_coordinate(bestchrom,2);plot(cargox,cargoy,'rs','LineWidth',2,... 'MarkerEdgeColor','r',... 'MarkerFaceColor','b',... 'MarkerSize',20)hold on
plot(city_coordinate(:,1),city_coordinate(:,2),'o','LineWidth',2,... 'MarkerEdgeColor','k',... 'MarkerFaceColor','g',... 'MarkerSize',10)
for i=1:31 x=[city_coordinate(i,1),city_coordinate(bestchrom(b(i)),1)]; y=[city_coordinate(i,2),city_coordinate(bestchrom(b(i)),2)]; plot(x,y,'c');hold onend1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798function fit=fitness(individual)%计算个体适应度%individual input 个体%fit output 适应度值%城市坐标city_coordinate=[1304,2312;3639,1315;4177,2244;3712,1399;3488,1535;3326,1556; 3238,1229;4196,1044;4312,790;4386,570;3007,1970;2562,1756; 2788,1491;2381,1676;1332,695;3715,1678;3918,2179;4061,2370; 3780,2212;3676,2578;4029,2838;4263,2931;3429,1908;3507,2376; 3394,2643;3439,3201;2935,3240;3140,3550;2545,2357;2778,2826; 2370,2975];%货物量carge=[20,90,90,60,70,70,40,90,90,70,60,40,40,40,20,80,90,70,100,50,50,50,80,70,80,40,40,60,70,50,30];%找出最近配送点for i=1:31 %dist函数就是欧式距离加权函数 distance(i,:)=dist(city_coordinate(i,:),city_coordinate(individual,:)');end[a,b]=min(distance');%计算费用for i=1:31 expense(i)=carge(i)*a(i);endfit=sum(expense)+4.0e+4*length(find(a>3000));
end1234567891011121314151617181920212223242526function resemble=similar(individual1,individual2)%计算个体individual1与individual2的相似度%individual1,individual2 input 两个个体%resemble output 相似度k=zeros(1,length(individual1));for i=1:length(individual1) if find(individual1(i)==individual2) k(i)=1; endendresemble=sum(k)/length(individual1);end123456789101112function concentration = concentration(i,M,individuals)% 计算个体浓度值% i input 第i个抗体% M input 种群规模% individuals input 个体% concentration output 浓度值
concentration=0;for j=1:M xsd=similar(individuals.chrom(i,:),individuals.chrom(j,:)); % 第i个体与种群个体间的相似度 % 相似度大于阀值 if xsd>0.7 concentration=concentration+1; endend
concentration=concentration/M;
end12345678910111213141516171819function exc = excellence(individuals,M,ps)%计算个体繁殖概率%individuals input 种群%M input 种群规模%ps input 多样性评价参数%exc output 繁殖概率fit = 1./individuals.fitness;sumfit = sum(fit);con = individuals.concentration;sumcon = sum(con);for i=1:M exc(i)=fit(i)/sumfit*ps+con(i)/sumcon*(1-ps);end
end1234567891011121314151617function ret=Select(individuals,sizepop)% 轮盘赌选择% individuals input : 种群信息% sizepop input : 种群规模% ret output : 选择后得到的种群
excellence=individuals.excellence;pselect=excellence./sum(excellence);% 事实上 pselect = excellence；
index=[]; for i=1:sizepop % 转sizepop次轮盘 pick=rand; while pick==0 pick=rand; end for j=1:sizepop pick=pick-pselect(j); if pick<0 index=[index j]; break; % 寻找落入的区间，此次转轮盘选中了染色体j end endend% 注意：在转sizepop次轮盘的过程中，有可能会重复选择某些染色体
individuals.chrom=individuals.chrom(index,:);individuals.fitness=individuals.fitness(index);individuals.concentration=individuals.concentration(index);individuals.excellence=individuals.excellence(index);ret=individuals;
end123456789101112131415161718192021222324252627282930313233function ret=Mutation(pmutation,chrom,sizepop,length1)% 变异操作% pmutation input : 变异概率% chrom input : 抗体群% sizepop input : 种群规模% iii input : 进化代数% MAXGEN input : 最大进化代数% length1 input : 抗体长度% ret output : 变异得到的抗体群% 每一轮for循环中，可能会进行一次变异操作，染色体是随机选择的，变异位置也是随机选择的for i=1:sizepop % 变异概率 pick=rand; while pick==0 pick=rand; end index=unidrnd(sizepop);
% 判断是否变异 if pick>pmutation continue; end pos=unidrnd(length1); while pos==1 pos=unidrnd(length1); end nchrom=chrom(index,:); nchrom(pos)=unidrnd(31); while length(unique(nchrom))==(length1-1) nchrom(pos)=unidrnd(31); end flag=test(nchrom); if flag==1 chrom(index,:)=nchrom; end end
ret=chrom;end1234567891011121314151617181920212223242526272829303132333435363738394041424344function newindividuals = incorporate(individuals,sizepop,bestindividuals,overbest)% 将记忆库中抗体加入，形成新种群% individuals input 抗体群% sizepop input 抗体数% bestindividuals input 记忆库% overbest input 记忆库容量
m = sizepop+overbest;newindividuals = struct('fitness',zeros(1,m), 'concentration',zeros(1,m),'excellence',zeros(1,m),'chrom',[]);
% 遗传操作得到的抗体for i=1:sizepop newindividuals.fitness(i) = individuals.fitness(i); newindividuals.concentration(i) = individuals.concentration(i); newindividuals.excellence(i) = individuals.excellence(i); newindividuals.chrom(i,:) = individuals.chrom(i,:); end% 记忆库中抗体for i=sizepop+1:m newindividuals.fitness(i) = bestindividuals.fitness(i-sizepop); newindividuals.concentration(i) = bestindividuals.concentration(i-sizepop); newindividuals.excellence(i) = bestindividuals.excellence(i-sizepop); newindividuals.chrom(i,:) = bestindividuals.chrom(i-sizepop,:); end
end1234567891011121314151617181920212223242526function psd=popinit(M,length) ss=[];for i=1:M a=randperm(31,length); ss(i,:)=a;endpsd=ss;
end123456789function rets=bestselect(individuals,m,n)% 初始化记忆库,依据excellence，将群体中高适应度低相似度的overbest个个体存入记忆库% m input 抗体数% n input 记忆库个体数\父代群规模% individuals input 抗体群% bestindividuals output 记忆库\父代群
% 精英保留策略，将fitness最好的s个个体先存起来，避免因其浓度高而被淘汰s=3;rets=struct('fitness',zeros(1,n), 'concentration',zeros(1,n),'excellence',zeros(1,n),'chrom',[]);[fitness,index] = sort(individuals.fitness);for i=1:s rets.fitness(i) = individuals.fitness(index(i)); rets.concentration(i) = individuals.concentration(index(i)); rets.excellence(i) = individuals.excellence(index(i)); rets.chrom(i,:) = individuals.chrom(index(i),:);end
% 剩余m-s个个体leftindividuals=struct('fitness',zeros(1,m-s), 'concentration',zeros(1,m-s),'excellence',zeros(1,m-s),'chrom',[]);for k=1:m-s leftindividuals.fitness(k) = individuals.fitness(index(k+s)); leftindividuals.concentration(k) = individuals.concentration(index(k+s)); leftindividuals.excellence(k) = individuals.excellence(index(k+s)); leftindividuals.chrom(k,:) = individuals.chrom(index(k+s),:);end
% 将剩余抗体按excellence值排序[excellence,index]=sort(1./leftindividuals.excellence);
% 在剩余抗体群中按excellence再选n-s个最好的个体for i=s+1:n rets.fitness(i) = leftindividuals.fitness(index(i-s)); rets.concentration(i) = leftindividuals.concentration(index(i-s)); rets.excellence(i) = leftindividuals.excellence(index(i-s)); rets.chrom(i,:) = leftindividuals.chrom(index(i-s),:);end
end123456789101112131415161718192021222324252627282930313233343536373839function ret=Cross(pcross,chrom,sizepop,length)% 交叉操作% pcorss input : 交叉概率% chrom input : 抗体群% sizepop input : 种群规模% length input : 抗体长度% ret output : 交叉得到的抗体群
% 每一轮for循环中，可能会进行一次交叉操作，随机选择染色体是和交叉位置，是否进行交叉操作则由交叉概率（continue）控制for i=1:sizepop % 随机选择两个染色体进行交叉 pick=rand; while prod(pick)==0 pick=rand(1); end if pick>pcross continue; end % 找出交叉个体 index(1)=unidrnd(sizepop); index(2)=unidrnd(sizepop); while index(2)==index(1) index(2)=unidrnd(sizepop); end % 选择交叉位置 pos=ceil(length*rand); while pos==1 pos=ceil(length*rand); end
% 个体交叉 chrom1=chrom(index(1),:); chrom2=chrom(index(2),:); k=chrom1(pos:length); chrom1(pos:length)=chrom2(pos:length); chrom2(pos:length)=k; % 满足约束条件赋予新种群 flag1=test(chrom(index(1),:)); flag2=test(chrom(index(2),:)); if flag1*flag2==1 chrom(index(1),:)=chrom1; chrom(index(2),:)=chrom2; end end
ret=chrom;end12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455function flag=test(code)% 检查个体是否满足距离约束% code input 个体% flag output 是否满足要求标志
city_coordinate=[1304,2312;3639,1315;4177,2244;3712,1399;3488,1535;3326,1556;3238,1229;4196,1044;4312,790;4386,570; 3007,1970;2562,1756;2788,1491;2381,1676;1332,695;3715,1678;3918,2179;4061,2370;3780,2212;3676,2578; 4029,2838;4263,2931;3429,1908;3507,2376;3394,2643;3439,3201;2935,3240;3140,3550;2545,2357;2778,2826;2370,2975];
flag=1;if max( max(dist( city_coordinate(code,:)') ) )>3000 flag=0;end
end
