function [pso F] = pso_2D()
% FUNCTION PSO --------USE Particle Swarm Optimization Algorithm
%global present;
% close all;
pop_size = 10; % pop_size 种群大小
part_size = 2; % part_size 粒子大小, ** =n-D
gbest = zeros(1,part_size+1); % gbest 当前搜索到的最小的值
max_gen = 80; % max_gen 最大迭代次数
region=zeros(part_size,2); % 设定搜索空间范围
region=[-3,3;-3,3]; % **每一维设定不同范围
rand('state',sum(100*clock)); % 重置随机数发生器状态
arr_present = ini_pos(pop_size,part_size); % present 当前位置,随机初始化,rand()的范围为0~1
v=ini_v(pop_size,part_size); % 初始化当前速度
pbest = zeros(pop_size,part_size+1); % pbest 粒子以前搜索到的最优值，最后一列包括这些值的适应度
w_max = 0.9; % w_max 权系数最大值
w_min = 0.4;
v_max = 2; % **最大速度,为粒子的范围宽度
c1 = 2; % 学习因子
c2 = 2; % 学习因子
best_record = zeros(1,max_gen); % best_record记录最好的粒子的适应度。
% ————————————————————————
% 计算原始种群的适应度,及初始化
% ————————————————————————
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)); %计算原始种群的适应度
% end
pbest = arr_present; %初始化各个粒子最优值
[best_value best_index] = min(arr_present(:,end)); %初始化全局最优，即适应度为全局最小的值，根据需要也可以选取为最大值
gbest = arr_present(best_index,:);
%v = zeros(pop_size,1); % v 速度
% ————————————————————————
% 迭代
% ————————————————————————
% global m;
% m = moviein(1000); %生成帧矩阵
x=[-3:0.01:3];
y=[-3:0.01:3];
z=@(x,y) 3*(1-x).^2.*exp(-(x.^2) - (y+1).^2) ...
- 10*(x/5 - x.^3 - y.^5).*exp(-x.^2-y.^2) ...
- 1/3*exp(-(x+1).^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),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),'*'); %改为三维
% axis([-2*pi,2*pi,-pi,pi,-50,10]);
% hold off;
pause(0.01);
% m(:,i) = getframe; %添加图形
w = w_max-(w_max-w_min)*i/max_gen;
% fprintf('# %i 代开始！\n',i);
% 确定是否对打散已经收敛的粒子群——————————————————————————————
reset = 0; % reset = 1时设置为粒子群过分收敛时将其打散，如果＝1则不打散
if reset==1
bit = 1;
for k=1:part_size
bit = bit&(range(arr_present(:,k))<0.1);
end
if bit==1 % bit=1时对粒子位置及速度进行随机重置
arr_present = ini_pos(pop_size,part_size); % present 当前位置,随机初始化
v = ini_v(pop_size,part_size); % 速度初始化
for k=1:pop_size % 重新计算适应度
arr_present(k,end) = fitness(arr_present(k,1:part_size));
end
warning('粒子过分集中！重新初始化……'); % 给出信息
display(i);
end
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)); % 粒子速度更新 (a)
% 判断v的大小，限制v的绝对值小于5————————————————————————————
c = find(abs(v)>6); %**最大速度设置，粒子的范围宽度
v(c) = sign(v(c))*6; %如果速度大于3.14则，速度为3.14
arr_present(j,1:part_size) = arr_present(j,1:part_size)+v(j,1:part_size); % 粒子位置更新 (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)) % 根据条件更新pbest,如果是最小的值为小于号,相反则为大于号
pbest(j,:) = arr_present(j,:);
end
end
[best best_index] = max(arr_present(:,end)); % 如果是最小的值为min,相反则为max
if best>gbest(end)&(Region_in(arr_present(best_index,:),region)) % 如果当前最好的结果比以前的好，则更新最优值gbest,如果是最小的值为小于号,相反则为大于号
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');
% ***************************************************************************
% 计算适应度
% ***************************************************************************
function fit = fitness(present)
fit=3*(1-present(1)).^2.*exp(-(present(1).^2) - (present(2)+1).^2) ... %**需要求极值的函数,本例即peaks函数
- 10*(present(1)/5 - present(1).^3 - present(2).^5).*exp(-present(1).^2-present(2).^2) ...
- 1/3*exp(-(present(1)+1).^2 - present(2).^2);
function ini_present=ini_pos(pop_size,part_size)
ini_present = 3*rand(pop_size,part_size+1); %初始化当前粒子位置,使其随机的分布在工作空间 %** 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)); %初始化当前粒子速度,使其随机的分布在速度范围内
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)); %计算原始种群的适应度
end
