HHO_HHO_HHO优化_HHO优化算法_hho算法_改进_

% Developed in MATLAB R2013b % Source codes demo version 1.0 % _____________________________________________________ % Main paper: % Harris hawks optimization: Algorithm and applications % Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, Huiling Chen % Future Generation Computer Systems, % DOI: https://doi.org/10.1016/j.future.2019.02.028 % https://www.sciencedirect.com/science/article/pii/S0167739X18313530 % _____________________________________________________ % You can run the HHO code online at codeocean.com https://doi.org/10.24433/CO.1455672.v1 % You can find the HHO code at https://github.com/aliasghar68/Harris-hawks-optimization-Algorithm-and-applications-.git % _____________________________________________________ % Author, inventor and programmer: Ali Asghar Heidari, % PhD research intern, Department of Computer Science, School of Computing, National University of Singapore, Singapore % Exceptionally Talented Ph. DC funded by Iran's National Elites Foundation (INEF), University of Tehran % 03-03-2019 % Researchgate: https://www.researchgate.net/profile/Ali_Asghar_Heidari % e-Mail: [email protected], [email protected], % e-Mail (Singapore): [email protected], [email protected] % _____________________________________________________ % Co-author and Advisor: Seyedali Mirjalili % % e-Mail: [email protected] % [email protected] % % Homepage: http://www.alimirjalili.com % _____________________________________________________ % Co-authors: Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Hui-Ling Chen % Homepage: http://www.evo-ml.com/2019/03/02/hho/ % _____________________________________________________ %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Harris's hawk optimizer: In this algorithm, Harris' hawks try to catch the rabbit. % T: maximum iterations, N: populatoin size, CNVG: Convergence curve % To run HHO: [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj) function [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj) disp('HHO is now tackling your problem') tic % initialize the location and Energy of the rabbit Rabbit_Location=zeros(1,dim); Rabbit_Energy=inf; k = 0; %Initialize the locations of Harris' hawks X=initialization(N,dim,ub,lb); CNVG=zeros(1,T); t=0; % Loop counter while t<T for i=1:size(X,1) % fitness of locations fitness=fobj(X(i,:)); % Update the location of Rabbit if fitness<Rabbit_Energy Rabbit_Energy=fitness; Rabbit_Location=X(i,:); end end if t>0 [Rabbit_Location_temp,Rabbit_Energy_temp] = NelderMead(fobj,Rabbit_Location); if Rabbit_Energy_temp<Rabbit_Energy Rabbit_Location = Rabbit_Location_temp'; Rabbit_Energy = Rabbit_Energy_temp; end end E1=2*(1-(t/T)); % factor to show the decreaing energy of rabbit % Update the location of Harris' hawks for i=1:size(X,1) E0 = 2*rand()-1; %-1<E0<1 if T == 500 if t < 300 Escaping_Energy = E1*(E0); % escaping energy of rabbit else if mod(t,2) Escaping_Energy = E1*(E0) + 0.6*(rand()*(1-k/200)); else Escaping_Energy = E1*(E0) - 0.6*(rand()*(1-k/200)); end k = k + 1; end end if T == 300 if t < 200 Escaping_Energy = E1*(E0); % escaping energy of rabbit else if mod(t,2) Escaping_Energy = E1*(E0) + 0.6*(rand()*(1-k/100)); else Escaping_Energy = E1*(E0) - 0.6*(rand()*(1-k/100)); end k = k + 1; end end if abs(Escaping_Energy)>=1 %% Exploration: % Harris' hawks perch randomly based on 2 strategy: q=rand(); if q<0.5 % perch based on other family members if N/10 == 3 rand_Hawk_index1 = floor(30*rand()+1); rand_Hawk_index2 = floor(30*rand()+1); rand_Hawk_index3 = floor(30*rand()+1); X_rand1 = X(rand_Hawk_index1,:,:); X_rand2 = X(rand_Hawk_index2,:,:); X_rand3 = X(rand_Hawk_index3,:,:); X(i,:) = ((abs(X(i,:)-X_rand1)+abs(X(i,:)-X_rand2)+abs(X(i,:)-X_rand3))./3) - X(i,:); end if N/10 == 5 rand_Hawk_index1 = floor(50*rand()+1); rand_Hawk_index2 = floor(50*rand()+1); rand_Hawk_index3 = floor(50*rand()+1); rand_Hawk_index4 = floor(50*rand()+1); rand_Hawk_index5 = floor(50*rand()+1); X_rand1 = X(rand_Hawk_index1,:,:); X_rand2 = X(rand_Hawk_index2,:,:); X_rand3 = X(rand_Hawk_index3,:,:); X_rand4 = X(rand_Hawk_index4,:,:); X_rand5 = X(rand_Hawk_index5,:,:); X(i,:) = ((abs(X(i,:)-X_rand1)+abs(X(i,:)-X_rand2)+abs(X(i,:)-X_rand3)+abs(X(i,:)-X_rand4)+abs(X(i,:)-X_rand5))./5) - X(i,:); end elseif q>=0.5 % perch on a random tall tree (random site inside group's home range) X(i,:)=(Rabbit_Location(1,:)-mean(X))-rand()*((ub'-lb')*rand+lb'); end elseif abs(Escaping_Energy)<1 %% Exploitation: % Attacking the rabbit using 4 strategies regarding the behavior of the rabbit %% phase 1: surprise pounce (seven kills) % surprise pounce (seven kills): multiple, short rapid dives by different hawks r=rand(); % probablity of each event if r>=0.5 && abs(Escaping_Energy)<0.5 % Hard besiege X(i,:)=(Rabbit_Location)-Escaping_Energy*abs(Rabbit_Location-X(i,:)); end if r>=0.5 && abs(Escaping_Energy)>=0.5 % Soft besiege Jump_strength=2*(1-rand()); % random jump strength of the rabbit X(i,:)=(Rabbit_Location-X(i,:))-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:)); end %% phase 2: performing team rapid dives (leapfrog movements) if r<0.5 && abs(Escaping_Energy)>=0.5 % Soft besiege % rabbit try to escape by many zigzag deceptive motions Jump_strength=2*(1-rand()); X1=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:)); if fobj(X1)<fobj(X(i,:)) % improved move? X(i,:)=X1; else % hawks perform levy-based short rapid dives around the rabbit X2=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:))+rand(1,dim).*Levy(dim); if (fobj(X2)<fobj(X(i,:))) % improved move? X(i,:)=X2; end end end if r<0.5 && abs(Escaping_Energy)<0.5 % Hard besiege % rabbit try to escape by many zigzag deceptive motions % hawks try to decrease their average location with the rabbit Jump_strength=2*(1-rand()); X1=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X)); if fobj(X1)<fobj(X(i,:)) % improved move? X(i,:)=X1; else % Perform levy-based short rapid dives around the rabbit