减法平均优化器.zip

% Subtraction-Average-Based Optimizer %%% Designed and Developed by Pavel Trojovsk? and Mohammad Dehghani %%% function[Best_score,Best_pos,SABO_curve]=SABO(N,T,lo,hi,m,fitness) lo=ones(1,m).*(lo); % Lower limit for variables hi=ones(1,m).*(hi); % Upper limit for variables %% INITIALIZATION for i=1:m X(:,i) = lo(i)+rand(N,1).*(hi(i) - lo(i)); % Initial population end for i =1:N L=X(i,:); fit(i)=fitness(L); end %% for t=1:T % algorithm iteration %% update: BEST proposed solution [Fbest , blocation]=min(fit); if t==1 xbest=X(blocation,:); % Optimal location fbest=Fbest; % The optimization objective function elseif Fbest<fbest fbest=Fbest; xbest=X(blocation,:); end %% DX=zeros(N,m); for i=1:N %% based om Eq(4) for j=1:N I=round(1+rand+rand); for d=1:m DX(i,d)=DX(i,d)+(X(j,d)-I.*X(i,d)).*sign(fit(i)-fit(j)); end end X_new_P1=X(i,:)+((rand(1,m).*DX(i,:))./(N)); X_new_P1 = max(X_new_P1,lo);X_new_P1 = min(X_new_P1,hi); %% update position based on Eq (5) L=X_new_P1; fit_new_P1=fitness(L); if fit_new_P1<fit(i) X(i,:) = X_new_P1; fit(i) = fit_new_P1; end %% end% end for i=1:N %% best_so_far(t)=fbest; average(t) = mean (fit); end Best_score=fbest; Best_pos=xbest; SABO_curve=best_so_far; end