哈里斯鹰算法(HHO)优化BP神经网络分类预测,HHO-BP分类预测，多特征输入模型 多特征输入单输出的二分类及多分类模型

function [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj) % 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 循环计时器 while t<T for i=1:size(X,1) % Check boundries 检查边界 FU=X(i,:)>ub; FL=X(i,:)<lb; X(i,:)=(X(i,:).*(~(FU+FL)))+ub.*FU+lb.*FL; % fitness of locations fitness(1,i)=fobj(X(i,:)); % Update the location of Rabbit if fitness(1,i)<Rabbit_Energy Rabbit_Energy=fitness(1,i); Rabbit_Location=X(i,:); end end E1=2*(1-(t/T)); % factor to show the decreaing energy of rabbit 显示家兔衰老活力的因子 % 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,:)); 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 硬包围 X(i,:)=(Rabbit_Location)-Escaping_Energy*abs(Rabbit_Location-X(i,:)); end if r>=0.5 && abs(Escaping_Energy)>=0.5 % Soft besiege 软包围 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 X2=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X))+rand(1,dim).*Levy(dim); if (fobj(X2)<fobj(X(i,:))), % improved move? X(i,:)=X2; end end end %% end end t=t+1; disp(['第',num2str(t),'次迭代']) 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 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