?1 簡介

2 部分代碼

%% --------------- All subfunctions are list below ------------------%% Get cuckoos by ramdom walkfunction nest=get_cuckoos(nest,best,Lb,Ub)% Levy flightsn=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);endend

3 仿真結果

4 參考文獻

[1]王慶喜, 儲澤楠. 基于動態布谷鳥搜索算法的PID控制器參數優化[J]. 計算機測量與控制, 2015, 23(4):4.

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