hopfieldtsp.m

用MatLab

%连续Hopfield网络解决TSP
function HopfieldTsp()
clc;
N=10;   %城市数
A=1.5;  %系数A
D=1;    %系数D
u0=0.02;    %神经元函数斜率
Step_t=0.1; %计算步长
MaxEpochs=20000;%迭代次数
%得到城市间距离矩阵
CityCood=rand(2,N); %城市坐标
DistanceMat=dist(CityCood',CityCood);   %城市间距离矩阵
U=0.2*rand(N,N)-0.1;%神经元输入初始值在0附近产生
for Count=1:MaxEpochs
    V=(1+tansig(U/u0))/2;
    E=CacuEnergy(V,DistanceMat,A,D);%计算能量
    delta_U=CacuDeltaU(V,DistanceMat,A,D,Step_t);%计算U的增量
    U=U+delta_U*Step_t;
end
[NewV,CheckRes]=RouteCheck(V);%检查V是否是有效路径
if(CheckRes<1)
    FinalE=CacuEnergy(NewV,DistanceMat,A,D);
    RouteLen=TotalRouteLength(NewV,CityCood);%计算路径的真实长度
    PlotRoute(NewV,CityCood);%绘制路径
else
    disp('路径无效!!');
end
%能量计算
function E=CacuEnergy(V,d,A,D)
[n,n]=size(V);
t1=sumsqr(sum(V,2)-1);
t2=sumsqr(sum(V,1)-1);
PermitV=V(:,2:n);
PermitV=[PermitV V(:,1)];
temp=d*PermitV;
t3=sum(sum(V.*temp));
E=0.5*(A*t1+A*t2+D+t3);
%计算U的增量
function d_U=CacuDeltaU(V,d,A,D,dt)
[n,n]=size(V);
t1=repmat(sum(V,2)-1,1,n);
t2=repmat(sum(V,1)-1,n,1);
PermitV=V(:,2:n);
PermitV=[PermitV V(:,1)];
t3=d*PermitV;
d_U=-dt*(A*t1+A*t2+D*t3);
%检查V是否是有效路径
function [NewV,CheckRes]=RouteCheck(V)
[rows,columns]=size(V);
NewV=zeros(rows,columns);
[XC,Order]=max(V);
for j=1:columns
    NewV(Order(j),j)=1;
end
SC=sum(NewV);
SR=sum(NewV');
CheckRes=sumsqr(SC-SR);
%绘制路径
function PlotRoute(V,CityCood)
figure;
title('连续Hopfield网络解决TSP');
xlabel('X坐标');
ylabel('Y坐标');
axis([0,1,0,1]);
axis on;
[xxx,order]=max(V);
NewCood=CityCood(:,order);
NewCood=[NewCood NewCood(:,1)];
plot(NewCood(1,:),NewCood(2,:),'o-');
%计算路径实际长度
function Len=TotalRouteLength(V,CityCood)
[xxx,order]=max(V);
NewCood=CityCood(:,order);
NewCood=[NewCood NewCood(:,1)];
[rows,columns]=size(NewCood);
Len=0;
for i=2:columns
    Len=Len+dist(NewCood(:,i-1)',NewCood(:,i));
end