模拟退火算法及禁忌搜索算法的matlab源程序

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1、%%%模拟退火算法源程序%此题以中国31省会城市的最短旅行路径为例：%clear;clc;function[MinD,BestPath]=MainAneal(pn)%CityPosition存储的为每个城市的二维坐标x和y；CityPosition=[13042312;36391315;41772244;37121399;34881535;33261556;32381229;...41961044;4312790;4386570;30071970;25621756;27881491;23811676;...1332695;37151678;39182179;40

2、612370;37802212;36762578;40292838;...42632931;34291908;35072376;33942643;34393201;29353240;31403550;...25452357;27782826;23702975];figure(1);plot(CityPosition(:,1),CityPosition(:,2),'o')m=size(CityPosition,1);%城市的数目%D=sqrt((CityPosition(:,ones(1,m))-CityPosition(:,ones(1,m))').^2+...

3、(CityPosition(:,2*ones(1,m))-CityPosition(:,2*ones(1,m))').^2);path=zeros(pn,m);fori=1:pnpath(i,:)=randperm(m);enditer_max=100;%im_max=5;%Len1=zeros(1,pn);Len2=zeros(1,pn);path2=zeros(pn,m);t=zeros(1,pn);T=1e5;tau=1e-5;N=1;whileT>=tauiter_num=1;m_num=1;whilem_num

4、_maxfori=1:pnLen1(i)=sum([D(path(i,1:m-1)+m*(path(i,2:m)-1))D(path(i,m)+m*(path(i,1)-1))]);path2(i,:)=ChangePath2(path(i,:),m);Len2(i)=sum([D(path2(i,1:m-1)+m*(path2(i,2:m)-1))D(path2(i,m)+m*(path2(i,1)-1))]);endR=rand(1,pn);iffind((Len2-Len1R)~=0)path(find((Len

5、2-Len1R)~=0),:)=path2(find((Len2-Len1R)~=0),:);%#okLen1(find((Len2-Len1R)~=0))=Len2(find((Len2-Len1R)~=0));[TempMinD,TempIndex]=min(Len1);TracePath(N,:)=path(TempIndex,:);%#okDistanc

6、e(N)=TempMinD;%#okN=N+1;elsem_num=m_num+1;endenditer_num=iter_num+1;T=T*0.9;end[MinD,Index]=min(Distance);BestPath=TracePath(Index,:);%disp(MinD)%画出路线图figure(2);plot(CityPosition(BestPath(1:end-1),1),CityPosition(BestPath(1:end-1),2),'r*-');functionp2=ChangePath2(p1,CityNum)wh

7、ile(1)R=unidrnd(CityNum,1,2);ifabs(R(1)-R(2))>0break;endendI=R(1);J=R(2);ifI

8、是一种全局逐步寻优算法,