基于向量加权平均值的高效优化算法附Matlab代码.zip

%--------------------------------------------------------------------------------------------------------------------------- % weIghted meaN oF vectOrs (INFO) % INFO: An Efficient Optimization Algorithm based on Weighted Mean of Vectors % Codes of INFO:http://imanahmadianfar.com/codes/ % Website and codes of INFO:http://www.aliasgharheidari.com/INFO.html % Iman Ahmadianfar, Ali asghar Heidari, Saeed Noushadian, Huiling Chen, and Amir H. Gandomi % Last update: 01-10-2022 % e-Mail: im.ahmadian@gmail.com,i.ahmadianfar@bkatu.ac.ir. % e-Mail: as_heidari@ut.ac.ir, aliasghar68@gmail.com, % %--------------------------------------------------------------------------------------------------------------------------- % Authors: Ali Asghar Heidari(as_heidari@ut.ac.ir, aliasghar68@gmail.com),Saeed Noushadian, Huiling Chen(chenhuiling.jlu@gmail.com), and Amir H Gandomi, %--------------------------------------------------------------------------------------------------------------------------- % After use, please cite to the main paper: % Iman Ahmadianfar, Ali asghar Heidari, Saeed Noushadian, Huiling Chen, Amir H. Gandomi % INFO: An Efficient Optimization Algorithm based on Weighted Mean of Vectors % Expert Systems With Applications, 116516, 2022, doi: https://doi.org/10.1016/j.eswa.2022.116516 (Q1, 5-Year Impact Factor: 6.95) %--------------------------------------------------------------------------------------------------------------------------- % You can also follow the paper for related updates in researchgate: % https://www.researchgate.net/profile/Iman_Ahmadianfar % https://www.researchgate.net/profile/Ali_Asghar_Heidari. % Website and codes of INFO:% http://www.aliasgharheidari.com/INFO.html % You can also use and compare with our other new optimization methods: %(INFO)-2020-http://www.imanahmadianfar.com/codes. %(GBO)-2020-http://www.imanahmadianfar.com/codes. %(INFO)-2022- http://www.aliasgharheidari.com/INFO.html %(RUN)-2021- http://www.aliasgharheidari.com/RUN.html %(HGS)-2021- http://www.aliasgharheidari.com/HGS.html %(SMA)-2020- http://www.aliasgharheidari.com/SMA.html %(HHO)-2019- http://www.aliasgharheidari.com/HHO.html function [Best_Cost,Best_X,Convergence_curve]=INFO(nP,MaxIt,lb,ub,dim,fobj) %% Initialization Cost=zeros(nP,1); M=zeros(nP,1); X=initialization(nP,dim,ub,lb); for i=1:nP Cost(i) = fobj(X(i,:)); M(i)=Cost(i); end [~, ind]=sort(Cost); Best_X = X(ind(1),:); Best_Cost = Cost(ind(1)); Worst_Cost = Cost(ind(end)); Worst_X = X(ind(end),:); I=randi([2 5]); Better_X=X(ind(I),:); Better_Cost=Cost(ind(I)); %% Main Loop of INFO for it=1:MaxIt alpha=2*exp(-4*(it/MaxIt)); % Eqs. (5.1) & % Eq. (9.1) M_Best=Best_Cost; M_Better=Better_Cost; M_Worst=Worst_Cost; for i=1:nP % Updating rule stage del=2*rand*alpha-alpha; % Eq. (5) sigm=2*rand*alpha-alpha; % Eq. (9) % Select three random solution A1=randperm(nP); A1(A1==i)=[]; a=A1(1);b=A1(2);c=A1(3); e=1e-25; epsi=e*rand; omg = max([M(a) M(b) M(c)]); MM = [(M(a)-M(b)) (M(a)-M(c)) (M(b)-M(c))]; W(1) = cos(MM(1)+pi)*exp(-abs(MM(1)/omg)); % Eq. (4.2) W(2) = cos(MM(2)+pi)*exp(-abs(MM(2)/omg)); % Eq. (4.3) W(3)= cos(MM(3)+pi)*exp(-abs(MM(3)/omg)); % Eq. (4.4) Wt = sum(W); WM1 = del.*(W(1).*(X(a,:)-X(b,:))+W(2).*(X(a,:)-X(c,:))+ ... % Eq. (4.1) W(3).*(X(b,:)-X(c,:)))/(Wt+1)+epsi; omg = max([M_Best M_Better M_Worst]); MM = [(M_Best-M_Better) (M_Best-M_Better) (M_Better-M_Worst)]; W(1) = cos(MM(1)+pi)*exp(-abs(MM(1)/omg)); % Eq. (4.7) W(2) = cos(MM(2)+pi)*exp(-abs(MM(2)/omg)); % Eq. (4.8) W(3) = cos(MM(3)+pi)*exp(-abs(MM(3)/omg)); % Eq. (4.9) Wt = sum(W); WM2 = del.*(W(1).*(Best_X-Better_X)+W(2).*(Best_X-Worst_X)+ ... % Eq. (4.6) W(3).*(Better_X-Worst_X))/(Wt+1)+epsi; % Determine MeanRule r = unifrnd(0.1,0.5); MeanRule = r.*WM1+(1-r).*WM2; % Eq. (4) if rand<0.5 z1 = X(i,:)+sigm.*(rand.*MeanRule)+randn.*(Best_X-X(a,:))/(M_Best-M(a)+1); z2 = Best_X+sigm.*(rand.*MeanRule)+randn.*(X(a,:)-X(b,:))/(M(a)-M(b)+1); else % Eq. (8) z1 = X(a,:)+sigm.*(rand.*MeanRule)+randn.*(X(b,:)-X(c,:))/(M(b)-M(c)+1); z2 = Better_X+sigm.*(rand.*MeanRule)+randn.*(X(a,:)-X(b,:))/(M(a)-M(b)+1); end % Vector combining stage u=zeros(1,dim); for j=1:dim mu = 0.05*randn; if rand <0.5 if rand<0.5 u(j) = z1(j) + mu*abs(z1(j)-z2(j)); % Eq. (10.1) else u(j) = z2(j) + mu*abs(z1(j)-z2(j)); % Eq. (10.2) end else u(j) = X(i,j); % Eq. (10.3) end end % Local search stage if rand<0.5 L=rand<0.5;v1=(1-L)*2*(rand)+L;v2=rand.*L+(1-L); % Eqs. (11.5) & % Eq. (11.6) Xavg=(X(a,:)+X(b,:)+X(c,:))/3; % Eq. (11.4) phi=rand; Xrnd = phi.*(Xavg)+(1-phi)*(phi.*Better_X+(1-phi).*Best_X); % Eq. (11.3) Randn = L.*randn(1,dim)+(1-L).*randn; if rand<0.5 u = Best_X + Randn.*(MeanRule+randn.*(Best_X-X(a,:))); % Eq. (11.1) else u = Xrnd + Randn.*(MeanRule+randn.*(v1*Best_X-v2*Xrnd)); % Eq. (11.2) end