基于遗传优化算法的带时间窗多车辆路线规划matlab仿真-优快云博客

1.程序功能描述

2.测试软件版本以及运行结果展示

3.核心程序

4.本算法原理

5.完整程序

1.程序功能描述

基于遗传优化算法的带时间窗多车辆路线规划matlab仿真，通过输入各个节点坐标，以及出发点到节点的时间窗，来进行优化，输出最优的路线规划结果。

2.测试软件版本以及运行结果展示

MATLAB2022A版本运行

最后优化结果如下：

路线1：0-5-3-7-8-10-11-9-6-4-2-1-0
路线2：0-13-17-18-19-15-16-14-12-0
路线3：0-20-24-25-27-29-30-28-26-23-22-21-0
路线4：0-32-33-31-35-37-38-39-36-34-0

3.核心程序

..............................................................................
for ij=1: Miter
    ij
    %计算适应度值
    Jobj  = func_fitness(X1,Nums,Time_start,Time_end,Time_win2,Time_service,dmat);
    J_min = min(Jobj);
    Jobj2 = 1./Jobj;
    %选择
    Xsel  = func_select(X1,Jobj2,pg);
    %交叉 
    Xsel  = func_crossover(Xsel,pc);
    %编译
    Xsel  = func_mut(Xsel,pm);
    %局部搜
    Xsel  = func_neighbor1(Xsel, Nums, Time_start, Time_end, Time_win2, Time_service, dmat);
    X1    = func_reins(X1,Xsel,Jobj);
    X1    = func_dealrepeat(X1);
    Jobj  = func_fitness(X1,Nums,Time_start,Time_end,Time_win2,Time_service,dmat);
    JJ(ij)= min(Jobj);
end

figure;
plot(1:5:Miter,JJ(1:5:end),'r->',...
    'LineWidth',1,...
    'MarkerSize',5,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.9,0.0]);
grid on
xlabel('GA优化迭代次数');
ylabel('适应度值');

Jobj    = func_fitness(X1,Nums,Time_start,Time_end,Time_win2,Time_service,dmat);
[~,Idxs]= min(Jobj);
X_best  = X1(Idxs(1),:);
[Vdraw,Nbest,dbest,~,~]=func_decode(X_best, Nums, Time_start, Time_end, Time_win2, Time_service, dmat);
disp(['车辆数: ', num2str(Nbest), ', 总距离: ', num2str(dbest)]);

 
Numc = Pxy(2:end,:);                                      
NV   = size(Vdraw,1);                                                  
figure
hold on;box on
title('优化路径')
hold on;
colors =[0.3,0.5,0.6;
         0.9,0.3,0.3;
         0.4,0.8,0.4;
         1.0,0.6,0.2;];
for i=1:NV
    part_seq=Vdraw{i};            
    len=length(part_seq);
    for j=0:len
        if j==0
            fprintf('%s','路线',num2str(i),':');
            fprintf('%d->',0);
            c1=Numc(part_seq(1),:);
            plot([Pxy(1,1),c1(1)],[Pxy(1,2),c1(2)],'-','color',colors(i,:));
        elseif j==len
            fprintf('%d->',part_seq(j));
            fprintf('%d',0);
            fprintf('\n');
            c_len=Numc(part_seq(len),:);
            plot([c_len(1),Pxy(1,1)],[c_len(2),Pxy(1,2)],'-','color',colors(i,:));
        else
            fprintf('%d->',part_seq(j));
            c_pre=Numc(part_seq(j),:);
            c_lastone=Numc(part_seq(j+1),:);
            plot([c_pre(1),c_lastone(1)],[c_pre(2),c_lastone(2)],'-','color',colors(i,:));
        end
    end
end
plot(Numc(:,1),Numc(:,2),'bs','linewidth',1);hold on;
plot(Pxy(1,1),Pxy(1,2),'r>',...
    'LineWidth',1,...
    'MarkerSize',12,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.9,0.0]);
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