离散粒子群算法（DPSO）的Matlab代码

function main() %% 初始化城市位置向量 city_nums=5; %% 初始化微粒群参数 particle_nums=100; %% 最大进化代数 max_iter=100; %% 速度位置更新方式 v=（c1max-i/itermax*(c1max-c1min)）*v+c2(Pbest-Pi)+c3(Pgbest-Pi); %% 速度由N*N的矩阵来表示； %% 微粒个数为particle_nums; %% position_Matrix(1:city_nums):当前微粒路径 %% position_Matrix(city_nums+1):当前微粒适应度值 %% position_Matrix(city_nums+2:2*city_nums+1):个体最优路径 %% position_Matrix(2*city_nums+2):个体最优适应度值 city_position_x_max=10;city_position_y_max=10;city_position=rand(city_nums,2); city_position(:,1)=city_position(:,1)*city_position_x_max; city_position(:,2)=city_position(:,2)*city_position_y_max; %% 初始化城市距离矩阵 city_distance=zeros(city_nums,city_nums); for i=1:city_nums for j=1:i city_distance(i,j)=sqrt((city_position(i,1)-city_position(j,1)).^2+(city_position(i,1)-city_position(j,1)).^2); city_distance(j,i)=city_distance(i,j); end end position_Matrix=zeros(2*city_nums+2,particle_nums); speed_current_Matrix=zeros(city_nums,city_nums,particle_nums); c_x=0.5;c_1_min=0.0;c_1_max=1;c_2=0.4;c_3=0.2; %% 初始化微粒的速度和位置 for i=1:particle_nums position_Matrix(1:city_nums,i)=wGenerate(city_nums); position_Matrix(city_nums+1,i)=calFitness(position_Matrix(1:city_nums,i),city_distance); position_Matrix(city_nums+2:2*city_nums+1,i)=position_Matrix(1:city_nums,i); position_Matrix(2*city_nums+2,i)=position_Matrix(city_nums+1,i); st_Matrix=ones(city_nums).*c_x-eye(city_nums)>rand(city_nums); speed_current_Matrix(:,:,i)=(st_Matrix+st_Matrix')>ones(city_nums); end [globalBestFitness,thisIndex]=min(position_Matrix(2*city_nums+2,:),[],2); globalBestPath=position_Matrix(city_nums+2:2*city_nums+1,thisIndex); pathPlot(globalBestPath,city_position) title(num2str(globalBestFitness)); %% 迭代过程 for iter=1:max_iter for i=1:particle_nums PbestLinkM=linkM(position_Matrix(city_nums+2:2*city_nums+1,i)); PgbestLinkM=linkM(globalBestPath); v_1_M=PM2VM(speed_current_Matrix(:,:,i),c_1_max-i./max_iter*(c_1_max-c_1_min)); v_2_M=PM2VM(sub_M(PbestLinkM,speed_current_Matrix(:,:,i)),c_2); v_3_M=PM2VM(sub_M(PgbestLinkM,speed_current_Matrix(:,:,i)),c_3); v_M=and_M(and_M(v_1_M,v_2_M),v_3_M); for j=1:city_nums for k=j:city_nums if(v_M(j,k)==1) position_Matrix(1:city_nums,i)=add_M(position_Matrix(1:city_nums,i),[j;k]); end end end position_Matrix(city_nums+1,i)=calFitness(position_Matrix(1:city_nums,i),city_distance); if(position_Matrix(city_nums+1,i)<position_Matrix(2*city_nums+2,i)) position_Matrix(city_nums+2:2*city_nums+1,i)=position_Matrix(1:city_nums,i); position_Matrix(2*city_nums+2,i)=position_Matrix(city_nums+1,i); end end [globalBestFitness,thisIndex]=min(position_Matrix(2*city_nums+2,:),[],2); globalBestPath=position_Matrix(city_nums+2:2*city_nums+1,thisIndex); % st_Matrix=ones(city_nums).*c_x-eye(city_nums)>rand(city_nums); % speed_current_Matrix(:,:,thisIndex)=(st_Matrix+st_Matrix')>ones(city_nums); end grid on; figure pathPlot(globalBestPath,city_position) title(num2str(globalBestFitness));