粒子群算法优化pid源码 matlab仿真.rar

clear clc n = 50; % 粒子群规模 bird_setp =50; % 最大迭代次数 dim = 3; % 维数 c2 =2; % 加速常数1 c1 =2; % 加速常数2 w =0.9; % 惯性权重 fitness=0*ones(n,bird_setp); % 初始化粒子群 R1 = rand(dim, n); R2 = rand(dim, n); current_fitness =0*ones(n,1); % 初始化群体位置和速度 current_position = 10*(rand(dim, n)-.5); velocity = .3*randn(dim, n) ; local_best_position = current_position ; % 个体评估 for i = 1:n current_fitness(i) = tracklsq(current_position(:,i)); end local_best_fitness = current_fitness ; [global_best_fitness,g] = min(local_best_fitness) ; for i=1:n globl_best_position(:,i) = local_best_position(:,g) ; end % 速度更新 velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position)); % 群体更新 current_position = current_position + velocity ; % 群体跟新评估 %% 主体循环，迭代寻优 iter = 0 ; while ( iter < bird_setp ) iter = iter + 1; for i = 1:n, current_fitness(i) = tracklsq(current_position(:,i)) ; end for i = 1 : n if current_fitness(i) < local_best_fitness(i) local_best_fitness(i) = current_fitness(i); local_best_position(:,i) = current_position(:,i) ; end end [current_global_best_fitness,g] = min(local_best_fitness); if current_global_best_fitness < global_best_fitness global_best_fitness = current_global_best_fitness; for i=1:n globl_best_position(:,i) = local_best_position(:,g); end end velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position)); current_position = current_position + velocity; sprintf('The value of interation iter %3.0f ', iter ); end % 结束 xx=fitness(:,50); [Y,I] = min(xx); current_position(:,I) %