布谷鸟.zip_Cuckoo neural_MATLAB 网络优化_optimization_布谷鸟 神经_布谷鸟算法

% ----------------------------------------------------------------- % Cuckoo Search (CS) algorithm by Xin-She Yang and Suash Deb % % Programmed by Xin-She Yang at Cambridge University % % Programming dates: Nov 2008 to June 2009 % % Last revised: Dec 2009 (simplified version for demo only) % % ----------------------------------------------------------------- % Papers -- Citation Details: % 1) X.-S. Yang, S. Deb, Cuckoo search via Levy flights, % in: Proc. of World Congress on Nature & Biologically Inspired % Computing (NaBIC 2009), December 2009, India, % IEEE Publications, USA, pp. 210-214 (2009). % http://arxiv.org/PS_cache/arxiv/pdf/1003/1003.1594v1.pdf % 2) X.-S. Yang, S. Deb, Engineering optimization by cuckoo search, % Int. J. Mathematical Modelling and Numerical Optimisation, % Vol. 1, No. 4, 330-343 (2010). % http://arxiv.org/PS_cache/arxiv/pdf/1005/1005.2908v2.pdf % ----------------------------------------------------------------% % This demo program only implements a standard version of % % Cuckoo Search (CS), as the Levy flights and generation of % % new solutions may use slightly different methods. % % The pseudo code was given sequentially (select a cuckoo etc), % % but the implementation here uses Matlab's vector capability, % % which results in neater/better codes and shorter running time. % % This implementation is different and more efficient than the % % the demo code provided in the book by % "Yang X. S., Nature-Inspired Metaheuristic Algoirthms, % % 2nd Edition, Luniver Press, (2010). " % % --------------------------------------------------------------- % % =============================================================== % % Notes: % % Different implementations may lead to slightly different % % behavour and/or results, but there is nothing wrong with it, % % as this is the nature of random walks and all metaheuristics. % % ----------------------------------------------------------------- % Additional Note: This version uses a fixed number of generation % % (not a given tolerance) because many readers asked me to add % % or implement this option. Thanks.% function [bestnest,fmin]=cuckoo_search_new(n) if nargin<1, % Number of nests (or different solutions) n=25; end % Discovery rate of alien eggs/solutions pa=0.25; %% Change this if you want to get better results N_IterTotal=1000; %% Simple bounds of the search domain % Lower bounds nd=15; Lb=-5*ones(1,nd); % Upper bounds Ub=5*ones(1,nd); % Random initial solutions for i=1:n, nest(i,:)=Lb+(Ub-Lb).*rand(size(Lb)); end % Get the current best fitness=10^10*ones(n,1); [fmin,bestnest,nest,fitness]=get_best_nest(nest,nest,fitness); N_iter=0; %% Starting iterations for iter=1:N_IterTotal, % Generate new solutions (but keep the current best) new_nest=get_cuckoos(nest,bestnest,Lb,Ub); [fnew,best,nest,fitness]=get_best_nest(nest,new_nest,fitness); % Update the counter N_iter=N_iter+n; % Discovery and randomization new_nest=empty_nests(nest,Lb,Ub,pa) ; % Evaluate this set of solutions [fnew,best,nest,fitness]=get_best_nest(nest,new_nest,fitness); % Update the counter again N_iter=N_iter+n; % Find the best objective so far if fnew<fmin, fmin=fnew; bestnest=best; end end %% End of iterations %% Post-optimization processing %% Display all the nests disp(strcat('Total number of iterations=',num2str(N_iter))); fmin bestnest %% --------------- All subfunctions are list below ------------------ %% Get cuckoos by ramdom walk function nest=get_cuckoos(nest,best,Lb,Ub) % Levy flights n=size(nest,1); % Levy exponent and coefficient % For details, see equation (2.21), Page 16 (chapter 2) of the book % X. S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010). beta=3/2; sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta); for j=1:n, s=nest(j,:); % This is a simple way of implementing Levy flights % For standard random walks, use step=1; %% Levy flights by Mantegna's algorithm u=randn(size(s))*sigma; v=randn(size(s)); step=u./abs(v).^(1/beta); % In the next equation, the difference factor (s-best) means that % when the solution is the best solution, it remains unchanged. stepsize=0.01*step.*(s-best); % Here the factor 0.01 comes from the fact that L/100 should the typical % step size of walks/flights where L is the typical lenghtscale; % otherwise, Levy flights may become too aggresive/efficient, % which makes new solutions (even) jump out side of the design domain % (and thus wasting evaluations). % Now the actual random walks or flights s=s+stepsize.*randn(size(s)); % Apply simple bounds/limits nest(j,:)=simplebounds(s,Lb,Ub); end %% Find the current best nest function [fmin,best,nest,fitness]=get_best_nest(nest,newnest,fitness) % Evaluating all new solutions for j=1:size(nest,1), fnew=fobj(newnest(j,:)); if fnew<=fitness(j), fitness(j)=fnew; nest(j,:)=newnest(j,:); end end % Find the current best [fmin,K]=min(fitness) ; best=nest(K,:); %% Replace some nests by constructing new solutions/nests function new_nest=empty_nests(nest,Lb,Ub,pa) % A fraction of worse nests are discovered with a probability pa n=size(nest,1); % Discovered or not -- a status vector K=rand(size(nest))>pa; % In the real world, if a cuckoo's egg is very similar to a host's eggs, then % this cuckoo's egg is less likely to be discovered, thus the fitness should % be related to the difference in solutions. Therefore, it is a good idea % to do a random walk in a biased way with some random step sizes. %% New solution by biased/selective random walks stepsize=rand*(nest(randperm(n),:)-nest(randperm(n),:)); new_nest=nest+stepsize.*K; for j=1:size(new_nest,1) s=new_nest(j,:); new_nest(j,:)=simplebounds(s,Lb,Ub); end % Application of simple constraints function s=simplebounds(s,Lb,Ub) % Apply the lower bound ns_tmp=s; I=ns_tmp<Lb; ns_tmp(I)=Lb(I); % Apply the upper bounds J=ns_tmp>Ub; ns_tmp(J)=Ub(J); % Update this new move s=ns_tmp; %% You can replace the following by your own functions % A d-dimensional objective function function z=fobj(u) %% d-dimensional sphere function sum_j=1^d (u_j-1)^2. % with a minimum at (1,1, ...., 1); z=sum((u-1).^2);