【BP分类】基于matlab哈里斯鹰算法优化BP神经网络HHO-BP数据分类预测【含Matlab源码 3364期】.zip

% Developed in MATLAB R2013b % Source codes demo version 1.0 % __________________________完整的代码，方可运行；可提供运行操作视频！适合小白！___________________________ % 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: as_heidari@ut.ac.ir, aliasghar68@gmail.com, % e-Mail (Singapore): aliasgha@comp.nus.edu.sg, t0917038@u.nus.edu % _____________________________________________________ % Co-author and Advisor: Seyedali Mirjalili % % e-Mail: ali.mirjalili@gmail.com % seyedali.mirjalili@griffithuni.edu.au % % 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; %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) % Check boundries X(i,:)= Bounds( X(i,:), lb, ub); % 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 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 Escaping_Energy=E1*(E0); % escaping energy of rabbit if abs(Escaping_Energy)>=1 %% Exploration: % Harris' hawks perch randomly based on 2 strategy: q=rand(); rand_Hawk_index = floor(N*rand()+1); X_rand = X(rand_Hawk_index, :); if q<0.5 % perch based on other family members X(i,:)=X_rand-rand()*abs(X_rand-2*rand()*X(i,:)); X(i,:)= Bounds( X(i,:), lb, ub); 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); X(i,:)= Bounds( X(i,:), lb, ub); 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,:)); X(i,:)= Bounds( X(i,:), lb, ub); 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,:)); X(i,:)= Bounds( X(i,:), lb, ub); 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,:)); X1 = Bounds( X1, lb, ub); 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); X2 = Bounds( X2, lb, ub); 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)); X1 = Bounds( X1, lb, ub); if fobj(X1)<fobj(X(i,:)) % improved move? X(i,:)=X1; else % Perform levy-based short rapid dives around the rabbit X2=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X))+rand(1,dim).*Levy(dim); X2 = Bounds( X2, lb, ub); if (fobj(X2)<fobj(X(i,:))), % improved move? X(i,:)=X2; end end end %% end end t=t+1; CNVG(t)=Rabbit_Energy; % Print the progress every 100 iterations % if mod(t,100)==0 % display(['At iteration ', num2str(t), ' the best fitness is ', num2str(Rabbit_Energy)]); % end end toc end % ___________________________________ function o=Levy(d) beta=1.5; sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta); u=randn(1,d)*sigma;v=randn(1,d);step=u./abs(v).^(1/beta); o=step; end