【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

1 简介

2 部分代码

%% --------------- 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

3 仿真结果

4 参考文献

[1]王庆喜, 储泽楠. 基于动态布谷鸟搜索算法的PID控制器参数优化[J]. 计算机测量与控制, 2015, 23(4):4.

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