1 簡介

優(yōu)化算法是解決優(yōu)化問題的有效隨機(jī)方法之一。在本文中，提出了一種新的基于群體的算法，稱為北蒼鷹優(yōu)化 (NGO) 算法，該算法模擬了北蒼鷹在獵物狩獵過程中的行為。這種狩獵策略包括獵物識別和追尾過程兩個(gè)階段。描述了所提出的 NGO 算法的各個(gè)步驟，然后提出了用于解決優(yōu)化問題的數(shù)學(xué)模型。在 68 個(gè)不同的目標(biāo)函數(shù)上評估 NGO 解決優(yōu)化問題的能力。

2 部分代碼

% 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謾lovsk媒2% 1Department of Mathematics, Faculty of Science, University of Hradec Kr謾lov茅, 50003 Hradec Kr謾lov茅, Czech Republic % 2Department of Applied Cybernetics, Faculty of Science, University of Hradec Kr謾lov茅, 50003 Hradec Kr謾lov茅, Czech Republic % " Optimizer"%%clcclearclose allSearchAgents=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 仿真結(jié)果

4 參考文獻(xiàn)

M. Dehghani, ?. Hubálovsky and P. Trojovsky, "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.

博主簡介：擅長智能優(yōu)化算法、神經(jīng)網(wǎng)絡(luò)預(yù)測、信號處理、元胞自動(dòng)機(jī)、圖像處理、路徑規(guī)劃、無人機(jī)等多種領(lǐng)域的Matlab仿真，相關(guān)matlab代碼問題可私信交流。

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