fourelemrents.7z

function [bestnest,fmin]=cuckoo_search_new(n,MatchedPoints1,MatchedPoints2) 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=9; Lb=-1*ones(1,nd); % Upper bounds Ub=1*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,MatchedPoints1,MatchedPoints2); 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,MatchedPoints1,MatchedPoints2); % 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,MatchedPoints1,MatchedPoints2); % 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,MatchedPoints1,MatchedPoints2) % Evaluating all new solutions for j=1:size(nest,1), fnew=fobj(newnest(j,:),MatchedPoints1,MatchedPoints2); 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;