【北方苍鹰算法】基于北方苍鹰算法求解单目标优化问题附matlab代码

 1 简介

优化算法是解决优化问题的有效随机方法之一。在本文中，提出了一种新的基于群体的算法，称为北苍鹰优化 (NGO) 算法，该算法模拟了北苍鹰在猎物狩猎过程中的行为。这种狩猎策略包括猎物识别和追尾过程两个阶段。描述了所提出的 NGO 算法的各个步骤，然后提出了用于解决优化问题的数学模型。在 68 个不同的目标函数上评估 NGO 解决优化问题的能力。

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2 部分代码

% DOI: 10.1109/ACCESS.2021.3133286
% Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems
% Mohammad Dehghani1, Pavel Trojovsk媒1, and Stepan Hub谩lovsk媒2
% 1Department of Mathematics, Faculty of Science, University of Hradec Kr谩lov茅, 50003 Hradec Kr谩lov茅, Czech Republic
% 2Department of Applied Cybernetics, Faculty of Science, University of Hradec Kr谩lov茅, 50003 Hradec Kr谩lov茅, Czech Republic
% " Optimizer"
%%
clc
clear
close all
SearchAgents=30;
Fun_name='F4';
Max_iterations=1000;
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);
[Score,Best_pos,NGO_curve]=NGO(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);
figure('Position',[300 300 660 290])
subplot(1,2,1);
fun_plot(Fun_name);
title('Objective space')
xlabel('x_1');
ylabel('x_2');
zlabel([Fun_name,'( x_1 , x_2 )'])
subplot(1,2,2);
plots=semilogx(NGO_curve,'Color','g');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');
axis tight
grid on
box on
legend('NGO')
display(['The best solution obtained by NGO is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by NGO is : ', num2str(Score)]);

3 仿真结果

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4 参考文献

M. Dehghani, Š. Hubálovský and P. Trojovský, "Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems," in IEEE Access, vol. 9, pp. 162059-162080, 2021, doi: 10.1109/ACCESS.2021.3133286.

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部分理论引用网络文献，若有侵权联系博主删除。

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