【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。

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"%%clcclearclose 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 ontitle('Objective space')xlabel('Iterations');ylabel('Best score');​axis tightgrid onbox onlegend('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

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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