基于随机漂移粒子群求解单目标优化问题附matlab代码

% RDPSO code % This code is written by Wael T. El Sayed, last update was on 12 % March 2018 % %------------------------------------------------------------------------------------- % Please, If you will use this code, then cite the following papers: %-------------- % 1- Paper no.1 %-------------- % Wael T. Elsayed, Yasser G. Hegazy, Mohamed S. El-bages, and Fahmy M. Bendary % “Improved Random Drift Particle Swarm Optimization With Self-Adaptive % Mechanism for Solving the Power Economic Dispatch Problem,” % IEEE Transactions on Industrial Informatics, vol. 13, no. 3, p. 1017 - 1026, 2017. %------------- % 2- Paper no.2 %------------- % J. Sun, V. Palade, X.-J. Wu, W. Fang, and Z. Wang, “Solving the power % economic dispatch problem with generator constraints by random drift % particle swarm optimization,” IEEE Transactions on Industrial Informatics, % vol. 10, no. 1, pp. 222–232, 2014. clc clear all % initialize the limits here. Lb = [-100 -100 -100 -100 -100]; %lower limits. Ub = [100 100 100 100 100]; %upper limits. % initialize the popoulation size. n = 10; % Size of the population. ndim = 5; % Dimension of the problem. %initialize the algorithm control parameters. NofRuns=2; % NofRuns is the required number of runs. Nofiter = 500; % Maximum number of iterations. % The range of thermal coefficient parameter see the paper no. 2 above page 226 % second column alphamax=0.9; alphamin=0.3; % Acceleration coefficients c1=2; c2=2; % Initialization to the results matrix. % results=[global cost per run,x1,x2,...,xn, time per run] results=zeros(NofRuns,ndim+2); jn=0; % Initialization to the loop variable for the runs. while jn<NofRuns %loop for the number of runs jn=jn+1; tic; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initialize the population within the limits for the current run. pop=zeros(n,ndim); for i=1:ndim pop(:,i)=Lb(i)+(Ub(i)-Lb(i))*rand(n,1); end % Evaluate the total population at the first iteration. cost=Sphere(pop'); %Initialize the local best equal to the initial population. localsolution = pop; % location of the local best. localcost = cost; % cost of local best. % Initialize the global solution. [globalcost,indx] = min(cost); globalsolution=pop(indx,:); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Start iterations iter = 0; %Initialization to the loop for the iterations while iter < Nofiter iter = iter + 1; alpha=alphamax-((alphamax-alphamin)/Nofiter)*iter; r1 = rand(n,ndim); % random numbers r2 = rand(n,ndim); % random numbers % The following to evaluate equation (15) in paper no. 1. phi= c1*r1./(c1*r1+c2*r2); % The following to evaluate equation (17) in paper no. 1. Cmean=sum(localsolution,1); Cmeant=(1/n)*repmat(Cmean,n,1); % The following to evaluate equation (14) in paper no. 1. localfocus=(phi.*localsolution)+(1-phi).*(repmat(globalsolution,n,1)); % Update the position and the velocity % The following to evaluate equation (19) in paper no. 1. vel = alpha*abs(Cmeant-pop).*normrnd(0,1,n,ndim)+1.5*(localfocus-pop); % The following to evaluate equation (13) in paper no. 1. pop = pop + vel; % Evaluate the total population at the current iteration. cost=Sphere(pop'); % The following uses equations (10) and (11) in paper no. 1 to update the local or % personal best positions for the particles and the global best % position. bettercost = cost < localcost; localcost = localcost.*not(bettercost) + cost.*bettercost; localsolution(bettercost,:) = pop(bettercost,:); % Update the global solution per each iteration [temp, t] = min(localcost); if temp<globalcost globalsolution=pop(t,:); indx=t; globalcost=temp; end end %End of the iterations loop % Handeling the final results % The results will be available in the workspace in the matrix results, % where results=[global cost per run,x1,x2,...,xn, time per run] time=toc; if NofRuns==1 results (1,1)=globalcost; results (1,2:(ndim+1))=globalsolution; results (1,(ndim+2))=time; else results (jn,1)=globalcost; results (jn,2:(ndim+1))=globalsolution; results (jn,(ndim+2))=time; end toc jn end % End of no of runs loop