clc

clear

load data z information 

%起点坐标

starty=5;

starth=3;

%终点坐标

endy=8;

endh=5;

n=10;

m=21;

Best=[];

[path,information]=searchpath(n,m,information,z,starty,starth,endy,endh);  %路径寻找

fitness=CacuFit(path);    %适应度计算

[bestfitness,bestindex]=min(fitness);           %最佳适应度

bestpath=path(bestindex,:);

Best=[Best;bestfitness];

% %更新信息素

rou=0.2;

cfit=100/bestfitness;

k=2;

for i=2:m-1

    information(k,bestpath(i*2-1),bestpath(i*2))=(1-rou)*information(k,bestpath(i*2-1),bestpath(i*2))+rou*cfit;

end

for kk=1:1:100

    kk

    [path,information]=searchpath(n,m,information,z,starty,starth,endy,endh);  %路径寻找

    fitness=CacuFit(path);    %适应度计算

    [newbestfitness,newbestindex]=min(fitness);           %最佳适应度

    if newbestfitness<bestfitness

        bestfitness=newbestfitness;

        bestpath=path(newbestindex,:);

    end

    Best=[Best;bestfitness];

    % %更新信息素

    rou=0.2;

    cfit=100/bestfitness;

    k=2;

    for i=2:m-1

        information(k,bestpath(i*2-1),bestpath(i*2))=(1-rou)*information(k,bestpath(i*2-1),bestpath(i*2))+rou*cfit;

    end

end

for i=1:21

    a(i,1)=bestpath(i*2-1);

    a(i,2)=bestpath(i*2);

end

k=1:21

figure(1)

x=1:21;

y=1:21;

[x1,y1]=meshgrid(x,y);

mesh(x1,y1,z)

load data z information 

axis([1,21,1,21,0,2000])

hold on

plot3(k',a(:,1)',a(:,2)'*200,'--o')

figure(2)

plot(Best)

title('最佳个体适应度变化趋势')

xlabel('迭代次数')

ylabel('适应度值')

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