【智能优化算法】基于凌日算法求解单目标优化问题附matlab代码Transit Search Optimization Algorithm

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1 内容介绍

Transit Search Optimization Algorithm 代码是从一种新颖的天体物理学启发的元启发式优化算法中提取出来的，该算法基于著名的系外行星探索方法，即凌日搜索（TS）。在凌日算法中，通过研究在一定间隔内从恒星接收到的光，检查亮度的变化，如果观察到接收到的光量减少，则表明行星从恒星锋面经过。为了评估该算法的性能，考虑了73个约束和无约束问题，并将结果与13个著名的优化算法进行了比较。这组示例包括各种类型的问题，包括数学函数（28个高维问题和15个低维问题）、CEC函数（10个问题）、约束数学基准问题（G01–G13）以及7个约束工程问题。结果表明，与其他有效算法相比，对于基准问题，该算法的总体平均误差是最低的

2 仿真代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% The following code are extracted from the reference below:

% https://authors.elsevier.com/sd/article/S2666-7207(22)00018-2

% Please cite this article as:

%  M. Mirrashid and H. Naderpour, Transit search: An optimization algorithm

%  based on exoplanet exploration; Results in Control and Optimization

%  (2022), doi: https://doi.org/10.1016/j.rico.2022.100127.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

clc; clear; close all

%% Definition of the Cost Function and its variables

Function_name='branin';   % Define your cost function (here is "Branin", a benchmark function"

[Vmin,Vmax,nV,Function] = CostFunction(Function_name);

CostFunction = @(x) Function(x);

%% Definition of the Algorithm Parameters

ns = 5;                 % Number of Stars

SN = 10;                % Signal to Noise Ratio

% Note: (ns*SN)=Number of population for the TS algorithm

maxcycle=500;           % max number of iterations

%% Transit Search Optimization Algorithm

disp('Transit Search is runing...')

[Bests] = TransitSearch (CostFunction,Vmin,Vmax,nV,ns,SN,maxcycle);

Best_Cost = Bests(maxcycle).Cost

Best_Solution = Bests(maxcycle).Location

%% Figure

figure = figure('Color',[1 1 1]);

G1=subplot(1,1,1,'Parent',figure);

x=zeros(maxcycle,1);

y=zeros(maxcycle,1);

for i = 1:maxcycle

    y(i,1) = Bests(i).Cost;

    x(i,1) = i;

end

plot(x,y,'r-','LineWidth',2);

xlabel('Iterations','FontWeight','bold','FontName','Times');

ylabel('Costs','FontWeight','bold','FontName','Times');

title (['Best Cost = ',num2str(Bests(maxcycle).Cost)])

box on

xlim ([1 maxcycle]);

ylim ([Bests(maxcycle).Cost Bests(1).Cost]);

set(G1,'FontName','Times','FontSize',20,'FontWeight','bold',...

    'XMinorGrid','on','XMinorTick','on','YMinorGrid','on','YMinorTick','on');

3 运行结果

4 参考文献

[1] Mirrashid M ,  Naderpour H . Transit search: An optimization algorithm based on exoplanet exploration[J]. Results in Control and Optimization, 2022.​

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