MLCCDE.zip

%************************************************************************************************** % Author: Wei Du % Last Edited: Jan 25, 2016 % Email: duwei0203@gmail.com % Reference: Differential Evolution With Event-Triggered Impulsive Control % IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2015.2512942 %************************************************************************************************** % The MATLAB source codes of JADE was provided by Dr. J. Zhang. % References: J. Zhang and A. C. Sanderson, "JADE: adaptive differential evolution with optional external archive," IEEE Trans. Evolut. Comput., vol. 13, no. 5, pp. 945-958, 2009. clc; clear all; tic; format long; format compact; warning off 'ETI-JADE' n=30; popsize=100; %migrate = 0.9; lu = [-100 * ones(1, n); 100 * ones(1, n)]; fhd=str2func('cec14_func'); problemSet = 1:30; for problemIndex = [17:30,1:16] problem = problemSet(problemIndex); filename = strcat(strcat('f',num2str(problem)),'_5f2_09.txt'); fp = fopen(filename,'a+'); func_num = problem; thediv = 5.0e-02; % Record the best results outcome = []; beishu = 10; MaxGen = n * 10000 * beishu / popsize; curdiv = 0.0; time = 1; % The total number of runs totalTime = 30; while time <= totalTime curbestfit = realmax();%%%%%%%%%%%%%%%%%%our parameters prebestfit = realmax(); curdiv = 1.0; prediv = 1.0; recdiv = 1.0; flag = 0; count = 0; %times = 10; sign = 0; everbestfitall = realmax(); everbestfitinrun = realmax();%%%%%%%%%%%%%%%%%%our parameters rand('state', sum(100 * clock)); % Initialize the main population popold = repmat(lu(1, :), popsize, 1) + rand(popsize, n) .* (repmat(lu(2, :) - lu(1, :), popsize, 1)); valParents = feval(fhd, popold', func_num) - problemIndex * 100; valParents = valParents'; gbest=min(valParents); c = 1/10; p = 0.05; CRm = 0.5; goodCR = []; Fm = 0.5; Afactor = 1; srpop=0; pbest_STG=zeros(popsize,1); LN=1; UN=popsize; num=LN; archive.NP = Afactor * popsize; % the maximum size of the archive archive.pop = zeros(0, n); % the solutions stored in te archive archive.funvalues = zeros(0, 1); % the function value of the archived solutions %% the values and indices of the best solutions [valBest, indBest] = sort(valParents, 'ascend'); FES = popsize;%%%%%%%%0; xxx = 0; curdiv = 0.0; for x =1 : n midpoint(x) = median(popold(:,x)); end distobest = 1 : popsize; for x = 1: popsize distobest (x)= 0; for y = 1 : n distobest(x) = distobest(x) + abs((popold(x,y) - midpoint(y))/(lu(2, y) - lu(1, y))); end distobest (x) = distobest (x) / n; curdiv = curdiv + distobest (x); end curdiv = curdiv / popsize; everbestfitall = min(valParents); fprintf(fp,'%d %e %e %e %e\r\n', FES, curdiv, mean(valParents), min(valParents),everbestfitall);% %g %g %d %d %,everbestfitall,everbestfitinrun,mycount,ourOpt); %ourOpt = 0; %curbestfit = valParents(1); %prebestfit = realmax(); %curbestchrom = popold(1, :); %preind = 0; while FES < n * 10000 * beishu pop = popold; % the old population becomes the current population valParents_temp=valParents; if FES > 1 && ~isempty(goodCR) && sum(goodF) > 0 % If goodF and goodCR are empty, pause the update CRm = (1 - c) * CRm + c * mean(goodCR); Fm = (1 - c) * Fm + c * sum(goodF .^ 2) / sum(goodF); % Lehmer mean end % Generate CR according to a normal distribution with mean CRm, and std 0.1 % Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1 [F, CR] = randFCR(popsize, CRm, 0.1, Fm, 0.1); r0 = [1 : popsize]; popAll = [pop; archive.pop]; [r1, r2] = gnR1R2(popsize, size(popAll, 1), r0); [r3, r4] = gnR1R2(popsize, popsize, r0);%%%%%%%% % Find the p-best solutions pNP = max(round(p * popsize), 2); % choose at least two best solutions randindex = ceil(rand(1, popsize) * pNP); % select from [1, 2, 3, ..., pNP] randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0 pbest = pop(indBest(randindex), :); % randomly choose one of the top 100p% solutions %%%%%%%%%%%%%%%%%%% prebestfit = curbestfit; %prebestchrom = curbestchrom; [curbestfit,ind] = min(valParents); %curbestchrom = popold(ind, :); if prebestfit < everbestfitinrun everbestfitinrun = prebestfit; %everbestchrominrun = prebestchrom; end if prebestfit < everbestfitall everbestfitall = prebestfit; end if sign == 0 if (curbestfit >= everbestfitinrun) || (curbestfit < everbestfitinrun && (everbestfitinrun - curbestfit) / everbestfitinrun < 1e-5) mycount = mycount +1; %disp(mycount); else mycount = 0; end upcount =500; if abs(everbestfitinrun - everbestfitall) < 1e-10 upcount = upcount * 2; end if mycount >= upcount sign = 1;%表示这一代开始回退 mycount = 0; if curdiv > thediv migrate = 1.0; else migrate = 0.9; end everbestfitinrun = realmax(); archive.pop = zeros(0, n); % the solutions stored in te archive archive.funvalues = zeros(0, 1); % the function value of the archived solutions end else curdiv = 0.0; for x =1 : n midpoint(x) = median(popold(:,x)); end distobest = 1 : popsize; for x = 1: popsize distobest (x)= 0; for y = 1 : n distobest(x) = distobest(x) + abs((popold(x,y) - midpoint(y))/(lu(2, y) - lu(1, y))); end distobest (x) = distobest (x) / n; curdiv = curdiv + distobest (x); end curdiv = curdiv / popsize; %disp(curdiv); end %%%%%%%%%%%%%%%%%%% % == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == == if sign == 0 vi = pop + F(:, ones(1, n)) .* (pbest - pop + pop(r1, :) - popAll(r2, :)); else vi = pop + F(:, ones(1, n)) .* (pop(r3, :) - pop(r4, :)); end vi = boundConstraint(vi, pop, lu); % == == == == = Crossover == == == == = mask = rand(popsize, n) > CR(:, ones(1, n)); % mask is used to indicate which elements of ui comes from the parent rows = (1 : popsize)'; cols = floor(rand(popsize, 1) * n)+1; % choose one position where the element of ui doesn't come from the parent jrand = sub2ind([popsize n], rows, cols); mask(jrand) = false; ui = vi; ui(mask) = pop(mask); if sign ==0 || (sign == 1 && curdiv > thediv) valOffspring = feval(fhd, ui',