www.gusucode.com > Weighted Differential Evolution Algorithm (WDE) > Weighted Differential Evolution Algorithm (WDE)/cc_wde/3D-baseline adjustment/algo_wde.m
%{ Weighted Differential Evolution Algorithm (WDE) Platform: Matlab 2018a Cite this algorithm as; ----------------------------- P Civicioglu, E Besdok, MA Gunen, UH Atasever, (2018), Weighted Differential Evolution Algorithm for Numerical Function Optimization ; A Comparative Study with Cuckoo Search, Arti?cial Bee Colony, Adaptive Differential Evolution, and Backtracking Search Optimization Algorithms, Neural Comput & Applic (2018). https://doi.org/10.1007/s00521-018-3822-5 see for pdf ----------------------------- https://link.springer.com/article/10.1007/s00521-018-3822-5#citeas Copyright Notice Copyright (c) 2018, P Civicioglu, E Besdok, MA Gunen, UH Atasever All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. %} function algo_wde(fnc,mydata,N,D,low,up,MaxEpk) %INITIALIZATION if numel(low)==1, low = low * ones(1,D); up = up * ones(1,D); end % this line must be adapted to your problem P = GenP(2*N,D,low,up); % see Eq.1 in [1] fitP = feval(fnc,P,mydata); % ------------------------------------------------------------------------------------------ for epk=1:MaxEpk j = randperm(2*N); k = j(1:N); l = j(N+1 : 2*N); trialP = P(k,:); fitTrialP = fitP(k); temp = trialP ; % memory for index = 1:N w = rand(N,1).^3; w = w ./ sum(w); temp(index,:) = sum( w .* P(l,:) ); end while 1, m = randperm(N); if sum(1:N == m, 2)==0 ==0, break; end, end E = temp - trialP(m,:) ; % recombination M = GenM(N,D); if rand<rand, F = randn(1,D).^3 ; else, F = randn(N,1).^3 ; end Trial = trialP + F .* M .* E; % re-scaling and shift % Trial = BoundaryControl(Trial,low,up) ; % see Algorithm-3 in [1] fitT = feval(fnc,Trial,mydata) ; ind = fitT < fitTrialP ; trialP(ind,:) = Trial(ind,:) ; fitTrialP(ind) = fitT(ind) ; fitP(k)=fitTrialP; P(k,:)=trialP; % keep the solutions [bestsol,ind] = min(fitP); best = P(ind,:); assignin('base','globalminimum',bestsol) assignin('base','globalminimizer',best) % display the results fprintf('WDE | %5.0f ---> %9.16f \n',epk,bestsol) ; end %epk return function M = GenM(N,D) M = zeros(N,D); for i=1:N if rand<rand, k = rand^3; else, k=1-rand^3; end V = randperm(D); j = V( 1:ceil(k*D) ); M(i,j) = 1; end function pop = GenP(N,D,low,up) pop = ones(N,D); for i = 1:N for j = 1:D pop(i,j) = rand * ( up(j) - low(j) ) + low(j); end end return function pop = BoundaryControl(pop,low,up) [popsize,dim] = size(pop); for i = 1:popsize for j = 1:dim F = rand.^3 ; if pop(i,j) < low(j), pop(i,j) = low(j) + F .* ( up(j)-low(j) ); end if pop(i,j) > up(j), pop(i,j) = up(j) + F .* ( low(j)-up(j)); end end end return