【鷹棲息優(yōu)化算法】基于鷹棲息優(yōu)化算法求解單目標(biāo)優(yōu)化問題附matlab代碼
1 簡介










2 部分代碼
clear all
clc
close all
SearchAgents_no=30; % Number of search agents 種群數(shù)量
Function_name='F2'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper) 設(shè)定適應(yīng)度函數(shù)
Max_iteration=1000; % Maximum numbef of iterations 設(shè)定最大迭代次數(shù)
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name); ?%設(shè)定邊界以及優(yōu)化函數(shù)
[Best_pos,Best_score,SSA_curve]=EPO_fun(Max_iteration,SearchAgents_no,lb,ub,dim,fobj); %開始優(yōu)化
figure('Position',[269 ? 240 ? 660 ? 290])
%Draw search space
subplot(1,2,1);
func_plot(Function_name);
title('Parameter space')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])
%Draw objective space
subplot(1,2,2);
semilogy(SSA_curve,'Color','g','linewidth',2)
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
legend('EPO');
axis tight
grid on
box on
3 仿真結(jié)果


4 參考文獻(xiàn)
[1] Khan A T , Senior S L , Stanimirovic P S , et al. Model-Free Optimization Using Eagle Perching Optimizer[J]. 2018.
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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