基于PSO三维极点搜索源码程序 - matlab算法设计

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标题：基于PSO三维极点搜索源码程序
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所属分类: 算法设计资源类型：文件大小: 2.23 KB 上传时间: 2016-01-24 22:34:57 下载次数: 6 资源积分：1分提供者: matlab源码基于PSO三维极点搜索源码程序

内容：
基于PSO三维极点搜索源码程序，程序员在编程的过程中可以参考学习使用，希望对IT程序员有用，此源码程序简单易懂、方便阅读，有很好的学习价值！

部分代码如下：

function [pso F] = pso()

%FUNCTION Optimization --------USE Particle Swarm Optimization Algorithm

%global present;

% close all;

pop_size = 10; % pop_size

part_size = 2; % partible_size , ** =n-D

gbest = zeros(1,part_size+1); % gbest the best value so far

max_gen = 80; % max_gen max generation

region=zeros(part_size,2); % define searching regions

region=[-1,1;-1,1]; % define the size of regions

rand('state',sum(100*clock)); % reset the state of random machine

arr_present = ini_pos(pop_size,part_size); % present position, random initialization,rand() from 0~1

v=ini_v(pop_size,part_size); % initialized velocity

pbest = zeros(pop_size,part_size+1); % pbest the best result, last column contain the parameter as well

w_max = 0.9; % w_max maximum of weight

w_min = 0.4; % w_min minumum of weitht

v_max = 2; % ** maximum of velocity

c1 = 2; % learning parameter 1

c2 = 2; % learning parameter 2

best_record = zeros(1,max_gen); % best_record remember the best particle's parameters

% ————————————————————————

% Calulate the paricles affinity and initialization

% ————————————————————————

arr_present(:,end)=ini_fit(arr_present,pop_size,part_size);

% for k=1:pop_size

% present(k,end) = fitness(present(k,1:part_size)); %

% end

pbest = arr_present; %initialized all best value for particles

[best_value best_index] = min(arr_present(:,end)); %initialize the global optima

gbest = arr_present(best_index,:);

%v = zeros(pop_size,1); % v

% ————————————————————————

% Evolving

% ————————————————————————

% global m;

% m = moviein(1000); %

x=[-1:0.01:1];

y=[-1:0.01:1];

z=@(x,y) cos(2 * pi .* x) .* cos(2 * pi .* y) .* exp(-0.1 * (x.^2 + y.^2));

for i=1:max_gen

grid on;

% plot3(x,y,z);

% subplot(121),ezmesh(z),hold on,grid on,plot3(arr_present(:,1),arr_present(:,2),arr_present(:,3),'*'),hold off;

% subplot(122),ezmesh(z),view([145,90]),hold on,grid on,plot3(arr_present(:,1),arr_present(:,2),arr_present(:,3),'*'),hold off;

ezmesh(z,[-1,1,-1,1]),hold on,grid on,plot3(arr_present(:,1),arr_present(:,2),arr_present(:,3),'*'),hold off;

drawnow

F(i)=getframe;

% ezmesh(z)

% % view([-37,90])

% hold on;

% grid on;

% % plot(-0.0898,0.7126,'ro');

% plot3(arr_present(:,1),arr_present(:,2),arr_present(:,3),'*');

% axis([-2*pi,2*pi,-pi,pi,-50,10]);

% hold off;

pause(0.01);

% m(:,i) = getframe; %

w = w_max-(w_max-w_min)*i/max_gen;

% fprintf('# %i ！\n',i);

% Decide whether reset the particles because they may converge——————————————————————————————

reset = 0; % reset = 1

if reset==1

bit = 1;

for k=1:part_size

bit = bit&(range(arr_present(:,k))<0.1);

end

if bit==1 % bit=1, reset position and velocity

arr_present = ini_pos(pop_size,part_size); % present position

v = ini_v(pop_size,part_size); % velocity initialization

for k=1:pop_size % re-calculate affinity

arr_present(k,end) = fitness(arr_present(k,1:part_size));

end

warning('Reset particle because they already converged……');

display(i);

end

for j=1:pop_size

v(j,:) = w.*v(j,:)+c1.*rand.*(pbest(j,1:part_size)-arr_present(j,1:part_size))...

+c2.*rand.*(gbest(1:part_size)-arr_present(j,1:part_size)); % renew velocity (a)

% The abs(velocity) must <5————————————————————————————

c = find(abs(v)>6);

v(c) = sign(v(c))*6; %if v>3.14则，v=3.14

arr_present(j,1:part_size) = arr_present(j,1:part_size)+v(j,1:part_size); % renew position (b)

arr_present(j,end) = fitness(arr_present(j,1:part_size));

if (arr_present(j,end)>pbest(j,end))&(Region_in(arr_present(j,:),region)) % renew pbest

pbest(j,:) = arr_present(j,:);

end

[best best_index] = max(arr_present(:,end));

if best>gbest(end)&(Region_in(arr_present(best_index,:),region)) % if the result better than before, save to gbest

gbest = arr_present(best_index,:);

end

best_record(i) = gbest(end);

end

pso = gbest;

display(gbest);

% figure;

% plot(best_record);

% movie2avi(F,'pso_2D1.avi','compression','MSVC');

% ***************************************************************************

% Calculate Affinity

% ***************************************************************************

function fit = fitness(present)

fit=cos(2 * pi .* present(1)) .* cos(2 * pi .* present(1)) .* exp(-0.1 * (present(1).^2 + present(2).^2));

function ini_present=ini_pos(pop_size,part_size)

ini_present = 3*rand(pop_size,part_size+1); %initialized particles positions, randomly separate them in the region %** 6即为自变量范围

function ini_velocity=ini_v(pop_size,part_size)

ini_velocity =3/2*(rand(pop_size,part_size)); %initialized particles velocity, randomly separate them in the region

function flag=Region_in(pos_present,region)

[m n]=size(pos_present);

flag=1;

for j=1:n-1

flag=flag&(pos_present(1:j)>=region(j,1))&(pos_present(1:j)<=region(j,2));

end

function arr_fitness=ini_fit(pos_present,pop_size,part_size)

for k=1:pop_size

arr_fitness(k,1) = fitness(pos_present(k,1:part_size)); %calculate the initial population affinity

end

文件列表（点击上边下载按钮，如果是垃圾文件请在下面评价差评或者投诉）:

基于PSO三维极点搜索源码程序/
基于PSO三维极点搜索源码程序/pso.m

关键词：三维 极点 源码

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关键词： 三维 极点 源码

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