pso.rar_PSO_PSO 优化函数_PSO 函数_PSO优化_pso 优化matlab

%PSO >> function for the PSO ALGORITHM % % USAGES: 1.) [fxmin, xmin, Swarm, history] = PSO(psoOptions); % 2.) [fxmin, xmin, Swarm, history] = PSO; % 3.) fxmin = PSO(psoOptions); % 3.) PSO % etc. % % Arguments : psoOptions--> A Matlab stucture containing all PSO related options. (see also: get_psoOptions) % Return Values : [fxmin, xmin, Swarm, history] % | | | |_The history of the algorithm. Depicts how the function value of GBest changes over the run. % | | |_The final Swarm. (A matrix containing co-ordinates of all particles) % | |_The co-ordinates of Best (ever) particle found during the PSO's run. % |__The objective value of Best (^xmin) particle. % % History : Author : JAG (Jagatpreet Singh) % Created on : 05022003 (Friday. 2nd May, 2003) % Comments : The basic PSO algorithm. % Modified on : 0710003 (Thursday. 10th July, 2003) % Comments : It uses psoOptions structure now. More organized. % % see also: get_psoOptions function [fxmin, xmin, Swarm, history] = PSO(psoOptions) %Globals global psoFlags; global psoVars; global psoSParameters; global notifications; upbnd = 600; % Upper bound for init. of the swarm lwbnd = 300; % Lower bound for init. of the swarm GM = 0; % Global minimum (used in the stopping criterion) ErrGoal = 1e-10; % Desired accuracy % %Initializations if nargin == 0 psoOptions = get_psoOptions; end %For Displaying if psoOptions.Flags.ShowViz global vizAxes; %Use the specified axes if using GUI or create a new global if called from command window vizAxes = plot(0,0, '.'); axis([-1000 1000 -1000 1000 -1000 1000]); %Initially set to a cube of this size axis square; grid off; set(vizAxes,'EraseMode','xor','MarkerSize',15); %Set it to show particles. pause(1); end %End Display initialization % Initializing variables success = 0; % Success Flag iter = 0; % Iterations' counter fevals = 0; % Function evaluations' counter % Using params--- % Determine the value of weight change w_start = psoOptions.SParams.w_start; %Initial inertia weight's value w_end = psoOptions.SParams.w_end; %Final inertia weight w_varyfor = floor(psoOptions.SParams.w_varyfor*psoOptions.Vars.Iterations); %Weight change step. Defines total number of iterations for which weight is changed. w_now = w_start; inertdec = (w_start-w_end)/w_varyfor; %Inertia weight's change per iteration % Initialize Swarm and Velocity SwarmSize = psoOptions.Vars.SwarmSize; Swarm = rand(SwarmSize, psoOptions.Vars.Dim)*(psoOptions.Obj.ub-psoOptions.Obj.lb) + psoOptions.Obj.lb; VStep = rand(SwarmSize, psoOptions.Vars.Dim); f2eval = psoOptions.Obj.f2eval; %The objective function to optimize. %Find initial function values. fSwarm = feval(f2eval, Swarm); fevals = fevals + SwarmSize; % Initializing the Best positions matrix and % the corresponding function values PBest = Swarm; fPBest = fSwarm; % Finding best particle in initial population [fGBest, g] = min(fSwarm); lastbpf = fGBest; Best = Swarm(g,:); %Used to keep track of the Best particle ever fBest = fGBest; history = [0, fGBest]; if psoOptions.Flags.Neighbor % Define social neighborhoods for all the particles for i = 1:SwarmSize lo = mod(i-psoOptions.SParam.Nhood:i+psoOptions.SParam.Nhood, SwarmSize); nhood(i,:) = [lo]; end nhood(find(nhood==0)) = SwarmSize; %Replace zeros with the index of last particle. end if psoOptions.Disp.Interval & (rem(iter, psoOptions.Disp.Interval) == 0) disp(sprintf('Iterations\t\tfGBest\t\t\tfevals')); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% THE PSO LOOP %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% while( (success == 0) & (iter <= psoOptions.Vars.Iterations) ) iter = iter+1; % Update the value of the inertia weight w if (iter<=w_varyfor) & (iter > 1) w_now = w_now - inertdec; %Change inertia weight end %%%%%%%%%%%%%%%%% % The PLAIN PSO % % Set GBest A = repmat(Swarm(g,:), SwarmSize, 1); %A = GBest. repmat(X, m, n) repeats the matrix X in m rows by n columns. B = A; %B wil be nBest (best neighbor) matrix % use neighborhood model % circular neighborhood is used if psoOptions.Flags.Neighbor for i = 1:SwarmSize [fNBest(i), nb(i)] = min(fSwarm( find(nhood(i)) )); B(i, :) = Swarm(nb(i), :); end end % Generate Random Numbers R1 = rand(SwarmSize, psoOptions.Vars.Dim); R2 = rand(SwarmSize, psoOptions.Vars.Dim); % Calculate Velocity if ~psoOptions.Flags.Neighbor %Normal VStep = w_now*VStep + psoOptions.SParams.c1*R1.*(PBest-Swarm) + psoOptions.SParams.c2*R2.*(A-Swarm); else %With neighborhood R3 = rand(SwarmSize, psoOptions.Vars.Dim); %random nos for neighborhood VStep = w_now*VStep + psoOptions.SParams.c1*R1.*(PBest-Swarm) + psoOptionsSParams.c2*R2.*(A-Swarm) + psoOptionsSParams.c3*R3.*(B-Swarm); end % Apply Vmax Operator for v > Vmax changeRows = VStep > psoOptions.SParams.Vmax; VStep(find(changeRows)) = psoOptions.SParams.Vmax; % Apply Vmax Operator for v < -Vmax changeRows = VStep < -psoOptions.SParams.Vmax; VStep(find(changeRows)) = -psoOptions.SParams.Vmax; % ::UPDATE POSITIONS OF PARTICLES:: Swarm = Swarm + psoOptions.SParams.Chi * VStep; % Evaluate new Swarm fSwarm = feval(f2eval, Swarm); fevals = fevals + SwarmSize; % Updating the best position for each particle changeRows = fSwarm < fPBest; fPBest(find(changeRows)) = fSwarm(find(changeRows)); PBest(find(changeRows), :) = Swarm(find(changeRows), :); lastbpart = PBest(g, :); % Updating index g [fGBest, g] = min(fPBest); %Update Best. Only if fitness has improved. if fGBest < lastbpf [fBest, b] = min(fPBest); Best = PBest(b,:); end %%OUTPUT%% if psoOptions.Save.Interval & (rem(iter, psoOptions.Save.Interval) == 0) history((size(history,1)+1), :) = [iter, fBest]; end if psoOptions.Disp.Interval & (rem(iter, psoOptions.Disp.Interval) == 0) disp(sprintf('%4d\t\t\t%.5g\t\t\t%5d', iter, fGBest, fevals)); end if psoOptions.Flags.ShowViz [fworst, worst] = max(fGBest); DrawSwarm(Swarm, SwarmSize, iter, psoOptions.Vars.Dim, Swarm(g,:), vizAxes); end %%TERMINATION%% if abs(fGBest-psoOptions.Obj.GM) <= psoOptions.Vars.ErrGoal %GBest success = 1; elseif abs(fBest-psoOptions.Obj.GM)<=psoOptions.Vars.ErrGoal %Best success = 1 else lastbpf = fGBest; %To be used to find Best end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% END OF PSO LOOP %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [fxmin, b] = min(fPBest); xmin = PBest(b, :); history = history(:,1); %Comment below line to Return Swarm. Uncomment to return previous best positions. % Swarm = PBest; %Return PBest