免疫优化算法在物流配送中心选址中的应用附Matlab代码.zip

%% 免疫优化算法在物流配送中心选址中的应用 %% 清空环境 clc clear close all %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; 4356,590;3020,1870;2162,1756;2188,1291;1381,1626;1352,685; 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]; %% 算法基本参数 sizepop=40; % 种群规模 overbest=10; % 记忆库容量 MAXGEN=200; % 迭代次数 pcross=0.5; % 交叉概率 pmutation=0.5; % 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,:),city_coordinate,carge); % 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 on plot(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) img =gcf; %获取当前画图的句柄 print(img, '-dpng', '-r600', './img1.png') %即可得到对应格式和期望dpi的图像 %% 画出配送中心选址图 %找出最近配送点 [m n]=size(city_coordinate); for i=1:m 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); end figure(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:m 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 on end img =gcf; %获取当前画图的句柄 print(img, '-dpng', '-r600', './img.png') %即可得到对应格式和期望dpi的图像