DELM分类基于matlab天鹰算法改进深度学习极限学习机AO-DELM数据分类【含Matlab源码 3041期】.zip

%_______________________________________________________________________________________% % Aquila Optimizer (AO) source codes (version 1.0) % % % % Developed in MATLAB R2015a (7.13) % % Author and programmer: % % Abualigah, L, Yousri, D, Abd Elaziz, M, Ewees, A, Al-qaness, M, Gandomi, A. % % e-Mail: Aligah.2020@gmail.com (Laith Abualigah) % % Homepage: % % 1- https://scholar.google.com/citations?user=39g8fyoAAAAJ&hl=en % % 2- https://www.researchgate.net/profile/Laith_Abualigah % % % % Main paper: % %_____________Aquila Optimizer: A novel meta-heuristic optimization algorithm___________% %_______________________________________________________________________________________% % Abualigah, L, Yousri, D, Abd Elaziz, M, Ewees, A, Al-qaness, M, Gandomi, A. (2021). % Aquila Optimizer: A novel meta-heuristic optimization algorithm. % Computers & Industrial Engineering. %_______________________________________________________________________________________% function [Best_FF,Best_P,conv]=AO(N,T,LB,UB,Dim,F_obj) Best_P=zeros(1,Dim); Best_FF=inf; X=initialization(N,Dim,UB,LB); Xnew=X; Ffun=zeros(1,size(X,1)); Ffun_new=zeros(1,size(Xnew,1)); t=1; alpha=0.1; delta=0.1; while t<T+1 for i=1:size(X,1) F_UB=X(i,:)>UB; F_LB=X(i,:)<LB; X(i,:)=(X(i,:).*(~(F_UB+F_LB)))+UB.*F_UB+LB.*F_LB; Ffun(1,i)=F_obj(X(i,:)); if Ffun(1,i)<Best_FF Best_FF=Ffun(1,i); Best_P=X(i,:); end end G2=2*rand()-1; % Eq. (16) G1=2*(1-(t/T)); % Eq. (17) to = 1:Dim; u = .0265; r0 = 10; r = r0 +u*to; omega = .005; phi0 = 3*pi/2; phi = -omega*to+phi0; x = r .* sin(phi); % Eq. (9) y = r .* cos(phi); % Eq. (10) QF=t^((2*rand()-1)/(1-T)^2); % Eq. (15) %------------------------------------------------------------------------------------- for i=1:size(X,1) %------------------------------------------------------------------------------------- if t<=(2/3)*T if rand <0.5 Xnew(i,:)=Best_P(1,:)*(1-t/T)+(mean(X(i,:))-Best_P(1,:))*rand(); % Eq. (3) and Eq. (4) Ffun_new(1,i)=F_obj(Xnew(i,:)); if Ffun_new(1,i)<Ffun(1,i) X(i,:)=Xnew(i,:); Ffun(1,i)=Ffun_new(1,i); end else %------------------------------------------------------------------------------------- Xnew(i,:)=Best_P(1,:).*Levy(Dim)+X((floor(N*rand()+1)),:)+(y-x)*rand; % Eq. (5) Ffun_new(1,i)=F_obj(Xnew(i,:)); if Ffun_new(1,i)<Ffun(1,i) X(i,:)=Xnew(i,:); Ffun(1,i)=Ffun_new(1,i); end end %------------------------------------------------------------------------------------- else if rand<0.5 Xnew(i,:)=(Best_P(1,:)-mean(X))*alpha-rand+((UB-LB)*rand+LB)*delta; % Eq. (13) Ffun_new(1,i)=F_obj(Xnew(i,:)); if Ffun_new(1,i)<Ffun(1,i) X(i,:)=Xnew(i,:); Ffun(1,i)=Ffun_new(1,i); end else %------------------------------------------------------------------------------------- Xnew(i,:)=QF*Best_P(1,:)-(G2*X(i,:)*rand)-G1.*Levy(Dim)+rand*G2; % Eq. (14) Ffun_new(1,i)=F_obj(Xnew(i,:)); if Ffun_new(1,i)<Ffun(1,i) X(i,:)=Xnew(i,:); Ffun(1,i)=Ffun_new(1,i); end end end end %------------------------------------------------------------------------------------- if mod(t,100)==0 display(['At iteration ', num2str(t), ' the best solution fitness is ', num2str(Best_FF)]); end conv(t)=Best_FF; t=t+1; 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