利用蚁群算法求解直流电机转速控制的PID参数simulink模型

%% Ant colony optimization % writen by Mr.Ammar Al-Jodah clc close all clear all %% ACO paramters n_iter=100; %number of iteration NA=30; % Number of Ants alpha=0.8; % alpha beta=0.2; % beta roh=0.7; % Evaporation rate n_param=3; % Number of paramters LB=[0.1 0.5 0.1]; % lower bound UB=[5 10 0.8]; % upper bound n_node=1000; % number of nodes for each param %% intializing some variables cost_best_prev=inf; ant = zeros(NA,n_param); cost = zeros(NA,1); tour_selected_param = zeros(1,n_param); param_mat = zeros(n_iter,n_param); Nodes = zeros(n_node,n_param); prob = zeros(n_node, n_param); %% Generating Nodes T=ones(n_node,n_param).*eps; % Phormone Matrix dT=zeros(n_node,n_param); % Change of Phormone for i=1:n_param Nodes(:,i) =linspace(LB(i),UB(i),n_node); % Node generation at equal spaced points end %% Iteration loop for iter=1:n_iter for tour_i=1:n_param prob(:,tour_i)= (T(:,tour_i).^alpha) .* ((1./Nodes(:,tour_i)).^beta); prob(:,tour_i)=prob(:,tour_i)./sum(prob(:,tour_i)); end for A=1:NA for tour_i=1:n_param node_sel=rand; node_ind=1; prob_sum=0; for j=1:n_node prob_sum=prob_sum+prob(j,tour_i); if prob_sum>=node_sel node_ind=j; break end end ant(A,tour_i)=node_ind; tour_selected_param(tour_i) = Nodes(node_ind, tour_i); end cost(A)=cost_func(tour_selected_param,0); clc disp(['Ant number: ' num2str(A)]) disp(['Ant Cost: ' num2str(cost(A))]) disp(['Ant Paramters: ' num2str(tour_selected_param)]) if iter~=1 disp(['iteration: ' num2str(iter)]) disp('_________________') disp(['Best cost: ' num2str(cost_best)]) for i=1:n_param tour_selected_param(i) = Nodes(ant(cost_best_ind,i), i); end disp(['Best paramters: ' num2str(tour_selected_param)]) end end [cost_best,cost_best_ind]=min(cost); % Elitsem if (cost_best>cost_best_prev) && (iter~=1) [cost_worst,cost_worst_ind]=max(cost); ant(cost_worst_ind,:)=best_prev_ant; cost_best=cost_best_prev; cost_best_ind=cost_worst_ind; else cost_best_prev=cost_best; best_prev_ant=ant(cost_best_ind,:) end dT=zeros(n_node,n_param); % Change of Phormone for tour_i=1:n_param for A=1:NA dT(ant(A,tour_i),tour_i)=dT(ant(A,tour_i),tour_i)+cost_best/cost(A); end end T= roh.*T + dT; %% Plots , this section will not effect the algorithem % you can remove it to speed up the run cost_mat(iter)=cost_best; figure(1) plot(cost_mat) figure(2) for i=1:n_param tour_selected_param(i) = Nodes(ant(cost_best_ind,i), i); end cost_func(tour_selected_param,1); %% store data param_mat(iter,:) = tour_selected_param; save('ACO_data.mat','cost_mat','param_mat') drawnow end