秃鹰搜索算法优化极限学习机(BES-ELM)分类预测（Matlab完整源码和数据）

function [BestSol Convergence_curve timep]=BES(nPop,MaxIt,low,high,dim,fobj) %nPop: size of population %MaxIt:number of iterations %low, high : space of Decision variables %dim : number of Decision variables %fobj : funcation % paper citation : Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2020). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53(3), 2237-2264.? st=cputime; % Initialize Best Solution BestSol.cost = inf; for i=1:nPop pop.pos(i,:) = low+(high-low).*rand(1,dim); pop.cost(i)=fobj(pop.pos(i,:)); if pop.cost(i) < BestSol.cost BestSol.pos = pop.pos(i,:); BestSol.cost = pop.cost(i); end end disp(num2str([0 BestSol.cost])) for t=1:MaxIt %% 1- select_space [pop BestSol s1(t)]=select_space(fobj,pop,nPop,BestSol,low,high,dim); %% 2- search in space [pop BestSol s2(t)]=search_space(fobj,pop,BestSol,nPop,low,high); %% 3- swoop [pop BestSol s3(t)]=swoop(fobj,pop,BestSol,nPop,low,high); Convergence_curve(t)=BestSol.cost; disp(num2str([t BestSol.cost])) ed=cputime; timep=ed-st; end function [pop BestSol s1]=select_space(fobj,pop,npop,BestSol,low,high,dim) Mean=mean(pop.pos); % Empty Structure for Individuals empty_individual.pos = []; empty_individual.cost = []; lm= 2; s1=0; for i=1:npop newsol=empty_individual; newsol.pos= BestSol.pos+ lm*rand(1,dim).*(Mean - pop.pos(i,:)); newsol.pos = max(newsol.pos, low); newsol.pos = min(newsol.pos, high); newsol.cost=fobj(newsol.pos); if newsol.cost<pop.cost(i) pop.pos(i,:) = newsol.pos; pop.cost(i)= newsol.cost; s1=s1+1; if pop.cost(i) < BestSol.cost BestSol.pos= pop.pos(i,:); BestSol.cost=pop.cost(i); end end end function [pop best s1]=search_space(fobj,pop,best,npop,low,high) Mean=mean(pop.pos); a=10; R=1.5; % Empty Structure for Individuals empty_individual.pos = []; empty_individual.cost = []; s1=0; for i=1:npop-1 A=randperm(npop); pop.pos=pop.pos(A,:); pop.cost=pop.cost(A); [x y]=polr(a,R,npop); newsol=empty_individual; Step = pop.pos(i,:) - pop.pos(i+1,:); Step1=pop.pos(i,:)-Mean; newsol.pos = pop.pos(i,:) +y(i)*Step+x(i)*Step1; newsol.pos = max(newsol.pos, low); newsol.pos = min(newsol.pos, high); newsol.cost=fobj(newsol.pos); if newsol.cost<pop.cost(i) pop.pos(i,:) = newsol.pos; pop.cost(i)= newsol.cost; s1=s1+1; if pop.cost(i) < best.cost best.pos= pop.pos(i,:); best.cost=pop.cost(i); end end end function [pop best s1]=swoop(fobj,pop,best,npop,low,high) Mean=mean(pop.pos); a=10; R=1.5; % Empty Structure for Individuals empty_individual.pos = []; empty_individual.cost = []; s1=0; for i=1:npop A=randperm(npop); pop.pos=pop.pos(A,:); pop.cost=pop.cost(A); [x y]=swoo_p(a,R,npop); newsol=empty_individual; Step = pop.pos(i,:) - 2*Mean; Step1= pop.pos(i,:)-2*best.pos; newsol.pos = rand(1,length(Mean)).*best.pos+x(i)*Step+y(i)*Step1; newsol.pos = max(newsol.pos, low); newsol.pos = min(newsol.pos, high); newsol.cost=fobj(newsol.pos); if newsol.cost<pop.cost(i) pop.pos(i,:) = newsol.pos; pop.cost(i)= newsol.cost; s1=s1+1; if pop.cost(i) < best.cost best.pos= pop.pos(i,:); best.cost=pop.cost(i); end end end function [xR yR]=swoo_p(a,R,N) th = a*pi*exp(rand(N,1)); r =th; %R*rand(N,1); xR = r.*sinh(th); yR = r.*cosh(th); xR=xR/max(abs(xR)); yR=yR/max(abs(yR)); function [xR yR]=polr(a,R,N) %// Set parameters th = a*pi*rand(N,1); r =th+R*rand(N,1); xR = r.*sin(th); yR = r.*cos(th); xR=xR/max(abs(xR)); yR=yR/max(abs(yR));