智能优化算法——混合领导优化算法（MatlabMatlab代码实现）

news2025/2/23 2:58:09

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

2.1 Python运行结果

2.2 Matlab代码实现

🎉3 参考文献

🌈4 Matlab代码、Python代码实现

💥1 概述

本文提出了一种新的优化算法，称为基于领导者的混合优化( HLBO )，它适用于优化挑战。HLBO的主要思想是在混合领导者的指导下对算法种群进行引导。将HLBO的阶段分为勘探和开采两个阶段进行数学建模。通过对23个不同类型的单峰和多峰标准测试函数的求解，检验HLBO在优化中的有效性。单峰函数的优化结果表明，HLBO在局部搜索中具有较高的开发能力，能够更好地收敛到全局最优；而多峰函数的优化结果表明，HLBO在全局搜索中具有较高的探索能力。

📚2 运行结果

2.1 Python运行结果

2.2 Matlab代码实现

部分代码：

Fun_name='F2'; % Name of the test function that can be from F1 to F23 
SearchAgents=10;  % Number of search agents
Max_iterations=1000; % Maximum numbef of iterations

% Load details of the selected benchmark function
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);
[Best_score,Best_pos,HLBO_curve]=HLBO(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);


display(['The best solution obtained by HLBO is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by HLBO is : ', num2str(Best_score)]);

%% Draw objective space
plots=semilogx(HLBO_curve,'Color','g');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');

axis tight
grid on
box on
legend('HLBO')

Fun_name='F2'; % Name of the test function that can be from F1 to F23
SearchAgents=10; % Number of search agents
Max_iterations=1000; % Maximum numbef of iterations

% Load details of the selected benchmark function
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);
[Best_score,Best_pos,HLBO_curve]=HLBO(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);

display(['The best solution obtained by HLBO is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by HLBO is : ', num2str(Best_score)]);

%% Draw objective space
plots=semilogx(HLBO_curve,'Color','g');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');

axis tight
grid on
box on
legend('HLBO')