核心提示：通过 PSO实现TSP问题优化...
clc;
clear;
close all;
warning off;
data=load('Oliver30.txt');
a=data(:,2);
b=data(:,3);
C=[a b]; %城市坐标矩阵
n=size(C,1); %城市数目
D=zeros(n,n); %城市距离矩阵
%L_best=ones(Nmax,1);
for i=1:n
for j=1:n
if i~=j
D(i,j)=((C(i,1)-C(j,1))^2+(C(i,2)-C(j,2))^2)^0.5;
end
D(j,i)=D(i,j);
end
end
Nmax=200;
m=10;
%% 初始化所有粒子
for i=1:m
x(i,:)=randperm(n); %粒子位置
end
F=fitness(x,C,D); %计算种群适应度
%xuhao=xulie(F) %最小适应度种群序号
a1=F(1);
a2=1;
for i=1:m
if a1>=F(i)
a1=F(i);
a2=i;
end
end
xuhao=a2;
Tour_pbest=x; %当前个体最优
Tour_gbest=x(xuhao,:) ; %当前全局最优路径
Pb=inf*ones(1,m); %个体最优记录
Gb=F(a2); %群体最优记录
xnew1=x;
N=1;
while N<=Nmax
%计算适应度
F=fitness(x,C,D);
for i=1:m
if F(i)<Pb(i)
Pb(i)=F(i); %将当前值赋给新的最佳值
Tour_pbest(i,:)=x(i,:);%将当前路径赋给个体最优路径
end
if F(i)<Gb
Gb=F(i);
Tour_gbest=x(i,:);
end
end
% nummin=xulie(Pb) %最小适应度种群序号
a1=Pb(1);
a2=1;
for i=1:m
if a1>=Pb(i)
a1=Pb(i);
a2=i;
end
end
nummin=a2;
Gb(N)=Pb(nummin); %当前群体最优长度
for i=1:m
%% 与个体最优进行交叉
c1=round(rand*(n-2))+1; %在[1,n-1]范围内随机产生一个交叉位
c2=round(rand*(n-2))+1;
while c1==c2
c1=round(rand*(n-2))+1; %在[1,n-1]范围内随机产生一个交叉位
c2=round(rand*(n-2))+1;
end
chb1=min(c1,c2);
chb2=max(c1,c2);
cros=Tour_pbest(i,chb1:chb2); %交叉区域矩阵
ncros=size(cros,2); %交叉区域元素个数
%删除与交叉区域相同元素
for j=1:ncros
for k=1:n
if xnew1(i,k)==cros(j)
xnew1(i,k)=0;
for t=1:n-k
temp=xnew1(i,k+t-1);
xnew1(i,k+t-1)=xnew1(i,k+t);
xnew1(i,k+t)=temp;
end
end
end
end
xnew=xnew1;
%插入交叉区域
for j=1:ncros
xnew1(i,n-ncros+j)=cros(j);
end
%判断产生新路径长度是否变短
dist=0;
for j=1:n-1
dist=dist+D(xnew1(i,j),xnew1(i,j+1));
end
dist=dist+D(xnew1(i,1),xnew1(i,n));
if F(i)>dist
x(i,:)=xnew1(i,:);
end
%% 与全体最优进行交叉
c1=round(rand*(n-2))+1; %在[1,n-1]范围内随机产生一个交叉位
c2=round(rand*(n-2))+1;
while c1==c2
c1=round(rand*(n-2))+1; %在[1,n-1]范围内随机产生一个交叉位
c2=round(rand*(n-2))+1;
end
chb1=min(c1,c2);
chb2=max(c1,c2);
cros=Tour_gbest(chb1:chb2); %交叉区域矩阵
ncros=size(cros,2); %交叉区域元素个数
%删除与交叉区域相同元素
for j=1:ncros
for k=1:n
if xnew1(i,k)==cros(j)
xnew1(i,k)=0;
for t=1:n-k
temp=xnew1(i,k+t-1);
xnew1(i,k+t-1)=xnew1(i,k+t);
xnew1(i,k+t)=temp;
end
end
end
end
xnew=xnew1;
%插入交叉区域
for j=1:ncros
xnew1(i,n-ncros+j)=cros(j);
end
%判断产生新路径长度是否变短
dist=0;
for j=1:n-1
dist=dist+D(xnew1(i,j),xnew1(i,j+1));
end
dist=dist+D(xnew1(i,1),xnew1(i,n));
if F(i)>dist
x(i,:)=xnew1(i,:);
end
%% 进行变异操作
c1=round(rand*(n-1))+1; %在[1,n]范围内随机产生一个变异位
c2=round(rand*(n-1))+1;
temp=xnew1(i,c1);
xnew1(i,c1)=xnew1(i,c2);
xnew1(i,c2)=temp;
%判断产生新路径长度是否变短
dist=0;
for j=1:n-1
dist=dist+D(xnew1(i,j),xnew1(i,j+1));
end
dist=dist+D(xnew1(i,1),xnew1(i,n));
%dist=dist(xnew1(i,:),D);
if F(i)>dist
x(i,:)=xnew1(i,:);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% F=(x,C,D) %计算种群适应度
%xuhao=xulie(F) %最小适应度种群序号
a1=F(1);
a2=1;
for i=1:m
if a1>=F(i)
a1=F(i);
a2=i;
end
end
xuhao=a2;
L_best(N)=min(F);
Tour_gbest=x(xuhao,:); %当前全局最优路径
N=N+1;
figure(1)
scatter(C(:,1),C(:,2));
hold on
plot([C(Tour_gbest(1),1),C(Tour_gbest(n),1)],[C(Tour_gbest(1),2),C(Tour_gbest(n),2)],'ms-','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g')
for ii=2:n
plot([C(Tour_gbest(ii-1),1),C(Tour_gbest(ii),1)],[C(Tour_gbest(ii-1),2),C(Tour_gbest(ii),2)],'ms-','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g')
end
hold off
figure(2)
plot(L_best);
% set(findobj('tag','N'),'string',num2str(N-1));%当前迭代次数
% set(findobj('tag','tour'),'string',num2str(Tour_gbest));%当前最优路径
% set(findobj('tag','L'),'string',num2str(min(L_best)));%当前最优路径长度 %%%这里的L_best是当前最优路径？？？
end
for j=1:Nmax
if j==1
Nbest=1;
elseif L_best(j)<L_best(j-1)
Nbest=j;
end
end
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