bat_algorithm_batalgorithm_蝙蝠算法_源码

function [best,fmin,N_iter]=bat_algorithm() n=25; % Population size, typically 10 to 40 蝙蝠个体数 N_gen=20; % Number of generations 迭代次数 % This frequency range determines the scalings. You should change these values if necessary Qmin=0; % Frequency minimum Qmax=1; % Frequency maximum % Iteration parameters 迭代参数 N_iter=0; % Total number of function evaluations 功能评价总数 % Dimension of the search variables 搜索维数 d=2; % Number of dimensions A=1+rand(n,1); % Loudness (constant or decreasing)响度，产生[1,2]的随机数 r=rand(n,1); % Pulse rate (constant or decreasing)脉冲率,产生[0,1]的随机数 Af = 0.9; Rf = 0.9; r0 = r; % Lower limit/bounds/ a vector Lb=-2.048*ones(1,d); % Upper limit/bounds/ a vector Ub=2.048*ones(1,d); % Initializing arrays 初始化数组 Q=zeros(n,1); % Frequency 频率 v=zeros(n,d); % Velocities 速度 % Initialize the population/solutions for i=1:n Sol(i,:)=Lb+(Ub-Lb).*rand(1,d); %rand(m*n)会生成 m*n的矩阵，矩阵元素是[0,10]随机数 Fitness(i)=Fun(Sol(i,:)); %测试函数的解作为适应度/评价函数 end % Find the initial best solution [fmin,I]=min(Fitness); %I 记录取得fmin的Fitness的位置，而这位置正是Sol中解的位置；fmin是Fitness中最小的值 best=Sol(I,:); %记录最好的解 % Start the iterations -- Bat Algorithm (essential part) % for t=1:N_gen % Loop over all bats/solutions for i=1:n Q(i)=Qmin+(Qmin-Qmax)*rand; v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i); S(i,:)=Sol(i,:)+v(i,:); % Apply simple bounds/limits Sol(i,:)=simplebounds(Sol(i,:),Lb,Ub); %越界检查 % Pulse rate if rand>r(i,1) % The factor 0.001 limits the step sizes of random walks S(i,:)=best+0.001*randn(1,d);%这里的新的蝙蝠个体是由当前全局最好的个体产生的 %论文中是以“上一代的蝙蝠体”+“响度的随机的倍数”，这里不再实现 end % Evaluate new solutions Fnew=Fun(S(i,:)); % Update if the solution improves, or not too loud if ((Fnew<=Fitness(i)) && (rand<A(i,1))) Sol(i,:)=S(i,:); Fitness(i)=Fnew; A(i,1) = Af*A(i,1); %对响度进行更新 r(i,1) = r0(i,1)*(1-exp(-1*Rf*t )); %对脉冲率进行更新 end % Update the current best solution if Fnew<=fmin best=S(i,:); fmin=Fnew; end end N_iter=N_iter+n; end % Output/display disp(['Number of evaluations: ',num2str(N_iter)]); disp(['Best =',num2str(best),' fmin=',num2str(fmin)]); % Application of simple limits/bounds 越界检查 function s=simplebounds(s,Lb,Ub) % Apply the lower bound vector ns_tmp=s; I=ns_tmp<Lb; ns_tmp(I)=Lb(I); % Apply the upper bound vector J=ns_tmp>Ub; ns_tmp(J)=Ub(J); % Update this new move s=ns_tmp; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Objective function: your own objective function can be written here % Note: When you use your own function, please remember to % change limits/bounds Lb and Ub (see lines 52 to 55) % and the number of dimension d (see line 51). % 在这里设置你自己函数，并且更新搜索区间上限和下限，以及自变量x的维度 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function y=Fun(x) % Rosenbrock函数 % 二维情况下，在点(1,1)处有全局最优解 . y=(1-x(1)^2)^2+100*(x(2)-x(1)^2)^2; plot(x(1),x(2),'.'); hold on; %%%%% ============ end ====================================