【IA TSP】免疫算法求解31城市旅行商问题【含Matlab源码 1149期】.zip

%%%%%%%%%%%%%%%%%%%%%%%免疫算法求解决TSP问题%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear all; %清除所有变量 close all; %清图 clc; %清屏 C=[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]; %31个省会城市坐标 N=size(C,1); %TSP问题的规模,即城市数目 D=zeros(N); %任意两个城市距离间隔矩阵 %%%%%%%%%%%%%%%%%%%%%求任意两个城市距离间隔矩阵%%%%%%%%%%%%%%%%%%%%% for i=1:N for j=1:N D(i,j)=((C(i,1)-C(j,1))^2+(C(i,2)-C(j,2))^2)^0.5; end end NP=200; %免疫个体数目 G=1000; %最大免疫代数 f=zeros(N,NP); %用于存储种群 for i=1:NP f(:,i)=randperm(N); %随机生成初始种群 end len=zeros(NP,1); %存储路径长度 for i=1:NP len(i)=func3(D,f(:,i),N); %计算路径长度 end [Sortlen,Index]=sort(len); Sortf=f(:,Index); %种群个体排序 gen=0; %免疫代数 Ncl=10; %克隆个数 %%%%%%%%%%%%%%%%%%%%%%%%%%%免疫循环%%%%%%%%%%%%%%%%%%%%%%%%%%%%% while gen<G for i=1:NP/2 %%%%%%%%%%%%选激励度前NP/2个体进行免疫操作%%%%%%%%%%%%%%% a=Sortf(:,i); Ca=repmat(a,1,Ncl); for j=1:Ncl p1=floor(1+N*rand()); p2=floor(1+N*rand()); while p1==p2 p1=floor(1+N*rand()); p2=floor(1+N*rand()); end tmp=Ca(p1,j); Ca(p1,j)=Ca(p2,j); Ca(p2,j)=tmp; end Ca(:,1)=Sortf(:,i); %保留克隆源个体 %%%%%%%%%%%%克隆抑制，保留亲和度最高的个体%%%%%%%%%%%%%% for j=1:Ncl Calen(j)=func3(D,Ca(:,j),N); end [SortCalen,Index]=sort(Calen); SortCa=Ca(:,Index); af(:,i)=SortCa(:,1); alen(i)=SortCalen(1); end %%%%%%%%%%%%%%%%%%%%%%%%%%种群刷新%%%%%%%%%%%%%%%%%%%%%%%%%% for i=1:NP/2 bf(:,i)=randperm(N); %随机生成初始种群 blen(i)=func3(D,bf(:,i),N); %计算路径长度 end %%%%%%%%%%%%%%%%%%%%免疫种群与新种群合并%%%%%%%%%%%%%%%%%%%%% f=[af,bf]; len=[alen,blen]; [Sortlen,Index]=sort(len); Sortf=f(:,Index); gen=gen+1; trace(gen)=Sortlen(1); end %%%%%%%%%%%%%%%%%%%%%%%%%%输出优化结果%%%%%%%%%%%%%%%%%%%%%%%%%%% Bestf=Sortf(:,1); %最优变量 Bestlen=trace(end); %最优值 figure for i=1:N-1 plot([C(Bestf(i),1),C(Bestf(i+1),1)],... [C(Bestf(i),2),C(Bestf(i+1),2)],'bo-'); hold on; end plot([C(Bestf(N),1),C(Bestf(1),1)],... [C(Bestf(N),2),C(Bestf(1),2)],'ro-'); title(['优化最短距离:',num2str(trace(end))]); figure,plot(trace) xlabel('迭代次数') ylabel('目标函数值') title('亲和度进化曲线')