日志
PSO算法
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function PSOforTSP
%初始化
Alpha=0.25; %个体经验保留概率
Beta=0.25; %全局经验保留概率
NC_max=200; %最大迭代次数
m=80; %微粒数
CityNum=10; %问题的规模（城市个数）
[dislist,Clist]=tsp(CityNum);
NC=1;%迭代计数器
R_best=zeros(NC_max,CityNum); %各代最佳路线
L_best=inf.*ones(NC_max,1);%各代最佳路线的长度
L_ave=zeros(NC_max,1);%各代路线的平均长度
%产生微粒的初始位置
for i=1:m
x(i,:)=randperm(CityNum);
L(i)=CalDist(dislist,x(i,:));
end
p=x; %p为个体最好解
pL=L;
[L_best(1,1) n_best]=min(L);
R_best(1,:)=x(n_best,:);
L_ave(1,1)=mean(L);
%初始交换序
v=ones(CityNum-1,2,m)*(round(rand*(CityNum-1))+1);
figure(1);
while NC<=NC_max %停止条件之一：达到最大迭代次数
for i=1:m
xnew(i,:)=changeFun(x(i,:),v(:,:,i));
A=changeNum(x(i,:),p(i,:));
Arand=randFun(A,Alpha);
xnew(i,:)=changeFun(xnew(i,:),Arand);
B=changeNum(x(i,:),R_best(NC,:));
Brand=randFun(B,Beta);
xnew(i,:)=changeFun(xnew(i,:),Brand);
v(:,:,i)=changeNum(x(i,:),xnew(i,:));
L(i)=CalDist(dislist,xnew(i,:));
if L(i)<pL(i)
p(i,:)=xnew(i,:);
pL(i)=L(i);
end
end
[L_bestnew n_best]=min(L);
R_bestnew=xnew(n_best,:);
L_ave(NC+1,1)=mean(L);
if L_bestnew<L_best(NC,1)
L_best(NC+1,1)=L_bestnew;
R_best(NC+1,:)=R_bestnew;
else
L_best(NC+1,1)=L_best(NC,1);
R_best(NC+1,:)=R_best(NC,:);
end
x=xnew;
drawTSP10(Clist,R_best(NC,:),L_best(NC,1),NC,0);
%pause;
NC=NC+1;
end
%输出结果
Pos=find(L_best==min(L_best));
Shortest_Route=R_best(Pos(1),:);
Shortest_Length=L_best(Pos(1));
figure(2);
plot([L_best L_ave]);
legend('最短距离','平均距离');
end
function xnew=changeFun(x,C);
changeLen=size(C,1);
xnew=x;
for i=1:changeLen
a=xnew(C(i,1));
xnew(C(i,1))=xnew(C(i,2));
xnew(C(i,2))=a;
end
end
function C=changeNum(x,y);
CityNum=size(x,2);
C=ones(CityNum-1,2);
for i=1:CityNum-1
pos=find(x==y(i));
C(i,:)=[i pos];
x=changeFun(x,C(i,:));
end
end
function v=randFun(v,w);
randLen=size(v,1);
for i=1:randLen
if rand>w
v(i,2)=v(i,1);
end
end
end
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