含仿真操作录像，基于共生生物搜索算法(SOS)的目标优化matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); rng('default'); %Problem Statement Npar = 3; VarLow=[-5.12 -5.12 -5.12]; VarHigh = [5.12 5.12 5.12]; % parameters n=50; MaxIter=50; XBest = rand(1,Npar).* (VarHigh - VarLow) + VarLow; XBestFit = fitnessFunc(XBest); GB = XBestFit; %Initialization of memory X=repmat(struct('Position',zeros(1,Npar),'Velocity',zeros(1,Npar)),n,1); t=cputime; %Initialize Random ecosystem for ii = 1:n X(ii).Position = rand(1,Npar).* (VarHigh - VarLow) + VarLow; X(ii).Position=limiter(X(ii).Position,VarHigh,VarLow); X(ii).Fit = fitnessFunc(X(ii).Position); if X(ii).Fit < XBestFit XBest = X(ii).Position; XBestFit = X(ii).Fit; end end %Main Loop for jj=1:MaxIter rng(jj); for ii=1:n %MUTUALISM PHASE j=randperm(n,1); %random organism to interact Mutual_Vector=(X(ii).Position+X(j).Position)/2; % mutual vector % new solutions Xinew = X(ii).Position+rand(1,Npar).*(XBest-Mutual_Vector); Xjnew = X(j).Position+rand(1,Npar).*(XBest-Mutual_Vector); % limit solutions within search space Xinew=limiter(Xinew,VarHigh,VarLow); Xjnew=limiter(Xjnew,VarHigh,VarLow); % find new fitness values Xinewfit=fitnessFunc(Xinew); Xjnewfit=fitnessFunc(Xjnew); % update organisms if better if Xinewfit<X(ii).Fit X(ii).Position=Xinew; X(ii).Fit=Xinewfit; if X(ii).Fit < XBestFit XBest = X(ii).Position; XBestFit = X(ii).Fit; end end if Xjnewfit<X(j).Fit X(j).Position=Xjnew; X(j).Fit=Xjnewfit; if X(j).Fit < XBestFit XBest = X(j).Position; XBestFit = X(j).Fit; end end %COMMENSALISM PHASE j=randperm(n,1); %random organism to interact Xinew = X(ii).Position+(rand).*(XBest-X(ii).Position); Xinew=limiter(Xinew,VarHigh,VarLow); % limit within search space Xinewfit=fitnessFunc(Xinew); % update fitness % update organism if better if Xinewfit<X(ii).Fit X(ii).Position=Xinew; X(ii).Fit=Xinewfit; if X(ii).Fit < XBestFit XBest = X(ii).Position; XBestFit = X(ii).Fit; end end %PARASITISM PHASE j=randperm(n,1); %random organism to become parasite Xpar=X(j).Position; Xm=randperm(Npar,1); %random vector dimension Xpar(Xm) = rand* (VarHigh(Xm) - VarLow(Xm)) + VarLow(Xm); % limit within search space Xpar=limiter(Xpar,VarHigh,VarLow); Xparfit=fitnessFunc(Xpar); % update fitness % update organism if better if Xparfit<X(ii).Fit X(ii).Position=Xpar; X(ii).Fit=Xparfit; if X(ii).Fit < XBestFit XBest = X(ii).Position; XBestFit = X(ii).Fit; end end end % store the best value in each iteration GB = [GB XBestFit]; end t1=cputime; fprintf('The time taken is %3.2f seconds \n',t1-t); fprintf('The best value is :'); XBest XBestFit figure plot(0:MaxIter,GB, 'linewidth',1.2); xlabel('Iterations'); ylabel('Objective Function (Cost)'); grid('on')