飞蛾扑火优化算法MFO-黄鱼优化算法YSGA-驾驶员优化算法DTBO-自然库特鸟优化算法CBNL-梯度优化优化算法GBO单目标

%--------------------------------------------------------------------------------------------------------------------------- % Author, inventor and programmer: Iman Ahmadianfar, % Assistant Professor, Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran % Researchgate: https://www.researchgate.net/profile/Iman_Ahmadianfar % e-Mail: im.ahmadian@gmail.com, i.ahmadianfar@bkatu.ac.ir, %--------------------------------------------------------------------------------------------------------------------------- % Co-author: % Omid Bozorg-Haddad(OBHaddad@ut.ac.ir) % Xuefeng Chu(xuefeng.chu@ndsu.edu) %--------------------------------------------------------------------------------------------------------------------------- % Please refer to the main paper: % Gradient-Based Optimizer: A New Metaheuristic Optimization Algorithm % SIman Ahmadianfar, Omid Bozorg-Haddad, Xuefeng Chu % Information Sciences,2020 % DOI: https://doi.org/10.1016/j.ins.2020.06.037 % https://www.sciencedirect.com/science/article/pii/S0020025520306241 % ------------------------------------------------------------------------------------------------------------ % Website of GBO: http://imanahmadianfar.com/ % You can find and run the GBO code online at http://imanahmadianfar.com/ % You can find the GBO paper at https://doi.org/10.1016/j.ins.2020.06.037 % Please follow the paper for related updates in researchgate: https://www.researchgate.net/profile/Iman_Ahmadianfar %--------------------------------------------------------------------------------------------------------------------------- %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [Best_Cost,Best_X,Convergence_curve]=GBO(nP,MaxIt,lb,ub,dim,fobj) %% Initialization nV = dim; % Number f Variables pr = 0.5; % Probability Parameter lb = ones(1,dim).*lb; % lower boundary ub = ones(1,dim).*ub; % upper boundary Cost = zeros(nP,1); X = initialization(nP,nV,ub,lb); %Initialize the set of random solutions Convergence_curve = zeros(1,MaxIt); for i=1:nP Cost(i) = fobj(X(i,:)); % Calculate the Value of Objective Function end [~,Ind] = sort(Cost); Best_Cost = Cost(Ind(1)); % Determine the vale of Best Fitness Best_X = X(Ind(1),:); Worst_Cost=Cost(Ind(end)); % Determine the vale of Worst Fitness Worst_X=X(Ind(end),:); %% Main Loop for it=1:MaxIt beta = 0.2+(1.2-0.2)*(1-(it/MaxIt)^3)^2; % Eq.(14.2) alpha = abs(beta.*sin((3*pi/2+sin(3*pi/2*beta)))); % Eq.(14.1) for i=1:nP A1 = fix(rand(1,nP)*nP)+1; % Four positions randomly selected from population r1 = A1(1);r2 = A1(2); r3 = A1(3);r4 = A1(4); Xm = (X(r1,:)+X(r2,:)+X(r3,:)+X(r4,:))/4; % Average of Four positions randomly selected from population ro = alpha.*(2*rand-1);ro1 = alpha.*(2*rand-1); eps = 5e-3*rand; % Randomization Epsilon DM = rand.*ro.*(Best_X-X(r1,:));Flag = 1; % Direction of Movement Eq.(18) GSR=GradientSearchRule(ro1,Best_X,Worst_X,X(i,:),X(r1,:),DM,eps,Xm,Flag); DM = rand.*ro.*(Best_X-X(r1,:)); X1 = X(i,:) - GSR + DM; % Eq.(25) DM = rand.*ro.*(X(r1,:)-X(r2,:));Flag = 2; GSR=GradientSearchRule(ro1,Best_X,Worst_X,X(i,:),X(r1,:),DM,eps,Xm,Flag); DM = rand.*ro.*(X(r1,:)-X(r2,:)); X2 = Best_X - GSR + DM; % Eq.(26) Xnew=zeros(1,nV); for j=1:nV ro=alpha.*(2*rand-1); X3=X(i,j)-ro.*(X2(j)-X1(j)); ra=rand;rb=rand; Xnew(j) = ra.*(rb.*X1(j)+(1-rb).*X2(j))+(1-ra).*X3; % Eq.(27) end % Local escaping operator(LEO) % Eq.(28) if rand<pr k = fix(rand*nP)+1; f1 = -1+(1-(-1)).*rand();f2 = -1+(1-(-1)).*rand(); ro = alpha.*(2*rand-1); Xk = unifrnd(lb,ub,1,nV);%lb+(ub-lb).*rand(1,nV); % Eq.(28.8) L1=rand<0.5;u1 = L1.*2*rand+(1-L1).*1;u2 = L1.*rand+(1-L1).*1; u3 = L1.*rand+(1-L1).*1; L2=rand<0.5; Xp = (1-L2).*X(k,:)+(L2).*Xk; % Eq.(28.7) if u1<0.5 Xnew = Xnew + f1.*(u1.*Best_X-u2.*Xp)+f2.*ro.*(u3.*(X2-X1)+u2.*(X(r1,:)-X(r2,:)))/2; else Xnew = Best_X + f1.*(u1.*Best_X-u2.*Xp)+f2.*ro.*(u3.*(X2-X1)+u2.*(X(r1,:)-X(r2,:)))/2; end end % Check if solutions go outside the search space and bring them back Flag4ub=Xnew>ub; Flag4lb=Xnew<lb; Xnew=(Xnew.*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; Xnew_Cost = fobj(Xnew); % Update the Best Position if Xnew_Cost<Cost(i) X(i,:)=Xnew; Cost(i)=Xnew_Cost; if Cost(i)<Best_Cost Best_X=X(i,:); Best_Cost=Cost(i); end end % Update the Worst Position if Cost(i)>Worst_Cost Worst_X= X(i,:); Worst_Cost= Cost(i); end end Convergence_curve(it) = Best_Cost; % Show Iteration Information disp(['Iteration ' num2str(it) ': Best Fitness = ' num2str(Convergence_curve(it))]); end end % _________________________________________________ % Gradient Search Rule function GSR=GradientSearchRule(ro1,Best_X,Worst_X,X,Xr1,DM,eps,Xm,Flag) nV = size(X,2); Delta = 2.*rand.*abs(Xm-X); % Eq.(16.2) Step = ((Best_X-Xr1)+Delta)/2; % Eq.(16.1) DelX = rand(1,nV).*(abs(Step)); % Eq.(16) GSR = randn.*ro1.*(2*DelX.*X)./(Best_X-Worst_X+eps); % Gradient search rule Eq.(15) if Flag == 1 Xs = X - GSR + DM; % Eq.(21) else Xs = Best_X - GSR + DM; end yp = rand.*(0.5*(Xs+X)+rand.*DelX); % Eq.(22.6) yq = rand.*(0.5*(Xs+X)-rand.*DelX); % Eq.(22.7) GSR = randn.*ro1.*(2*DelX.*X)./(yp-yq+eps); % Eq.(23) end