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📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
本文采用蝙蝠算法、粒子群优化、花轮询算法和布谷鸟搜索算法，对管壳式换热器的控制系统进行了建模和计算机仿真。为了评估不同调整方法的性能，本文比较了生成的八个网格设置中的阶跃响应瞬态值。它还使用文献中提出的性能指标对这两种类型的网格进行了比较，通过蝙蝠算法优化的系统获得了与粒子群优化、布谷鸟搜索算法和花朵轮询算法相关的最佳瞬时值。性能指标FPA和PSO获得了较好的结果。
📚2 运行结果
部分代码：
% Draw n Levy flight sample
function L = Levy(d)
% Levy exponent and coefficient
% For details, see Chapter 11 of the following book:
% Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, (2014).
beta=3/2;
sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta);
u=randn(1,d)*sigma;
v=randn(1,d);
step=u./abs(v).^(1/beta);
L=0.01*step;
end
%% --------------- 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 参考文献
部分理论来源于网络，如有侵权请联系删除。
🌈4 Matlab代码及文章讲解
链接：https://pan.baidu.com/s/1PWA9hFsoxthlIpSDttH-Gg
提取码：4jq2
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