遗传算法MATLAB仿真程序(共5页).docx
精选优质文档-倾情为你奉上%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01 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专心-专注-专业
