蜜罐算法2021年新兴的群智能算法 效果好

function [Xprey, Food_Score,CNVG] = HBA(objfunc, dim,lb,ub,tmax,N) beta = 6; % the ability of HB to get the food Eq.(4) C = 2; %constant in Eq. (3) vec_flag=[1,-1]; %initialization X=initialization(N,dim,ub,lb); %Evaluation fitness = fun_calcobjfunc(objfunc, X); [GYbest, gbest] = min(fitness); Xprey = X(gbest,:); for t = 1:tmax alpha=C*exp(-t/tmax); %density factor in Eq. (3) I=Intensity(N,Xprey,X); %intensity in Eq. (2) for i=1:N r =rand(); F=vec_flag(floor(2*rand()+1)); for j=1:1:dim di=((Xprey(j)-X(i,j))); if r<.5 r3=rand; r4=rand; r5=rand; Xnew(i,j)=Xprey(j) +F*beta*I(i)* Xprey(j)+F*r3*alpha*(di)*abs(cos(2*pi*r4)*(1-cos(2*pi*r5))); else r7=rand; Xnew(i,j)=Xprey(j)+F*r7*alpha*di; end end FU=Xnew(i,:)>ub;FL=Xnew(i,:)<lb;Xnew(i,:)=(Xnew(i,:).*(~(FU+FL)))+ub.*FU+lb.*FL; tempFitness = fun_calcobjfunc(objfunc, Xnew(i,:)); if tempFitness<fitness(i) fitness(i)=tempFitness; X(i,:)= Xnew(i,:); end end FU=X>ub;FL=X<lb;X=(X.*(~(FU+FL)))+ub.*FU+lb.*FL; [Ybest,index] = min(fitness); CNVG(t)=min(Ybest); if Ybest<GYbest GYbest=Ybest; Xprey = X(index,:); end end Food_Score = GYbest; end function Y = fun_calcobjfunc(func, X) N = size(X,1); for i = 1:N Y(i) = func(X(i,:)); end end function I=Intensity(N,Xprey,X) for i=1:N-1 di(i) =( norm((X(i,:)-Xprey+eps))).^2; S(i)=( norm((X(i,:)-X(i+1,:)+eps))).^2; end di(N)=( norm((X(N,:)-Xprey+eps))).^2; S(N)=( norm((X(N,:)-X(1,:)+eps))).^2; for i=1:N r2=rand; I(i)=r2*S(i)/(4*pi*di(i)); end end function [X]=initialization(N,dim,up,down) if size(up,2)==1 X=rand(N,dim).*(up-down)+down; end if size(up,2)>1 for i=1:dim high=up(i);low=down(i); X(:,i)=rand(N,1).*(high-low)+low; end end end