【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

% 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)]);