【PID优化】基于蝙蝠 粒子群 花卉授粉算法和布谷鸟搜索算法实现热交换器的PI控制器优化

%% --------------- 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 end