【智能优化算法-天鹰算法】基于天鹰优化算法求解多目标优化问题附matlab代码

1 内容介绍

本文提出了一种新的基于种群的优化方法，称为 Aquila Optimizer (AO)，它是灵感来自天鹰座在捕捉猎物过程中的自然行为。因此，优化所提出的AO算法的程序用四种方法表示；按高选择搜索空间垂直俯冲翱翔，通过短滑翔攻击的轮廓飞行在发散的搜索空间内探索，通过低速飞行和慢速下降攻击在收敛搜索空间内利用，并通过步行和俯冲抓住猎物。

2 仿真代码

% ----------------------------------------------------------------------- %

function nd_pop=GetNonDominatedParticles(pop)

ND=~[pop.Dominated]; nd_pop=pop(ND);

end

%__________________________________________________________________ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% ----------------------------------------------------------------------- %

function dom=Dominates(x,y)

if isstruct(x) x=x.Cost; end

if isstruct(y) y=y.Cost; end dom=all(x<=y) && any(x<y);

end

% ----------------------------------------------------------------------- %

function [Archive_X_Chopped, Archive_F_Chopped, Archive_mem_ranks_updated, Archive_member_no]=HandleFullArchive(Archive_X, Archive_F, Archive_member_no, Archive_mem_ranks, ArchiveMaxSize)

for i=1:size(Archive_F,1)-ArchiveMaxSize
index=RouletteWheelSelection(Archive_mem_ranks);

Archive_X=[Archive_X(1:index-1,:) ; Archive_X(index+1:Archive_member_no,:)];
Archive_F=[Archive_F(1:index-1,:) ; Archive_F(index+1:Archive_member_no,:)];
Archive_mem_ranks=[Archive_mem_ranks(1:index-1) Archive_mem_ranks(index+1:Archive_member_no)];
Archive_member_no=Archive_member_no-1;
end

Archive_X_Chopped=Archive_X;
Archive_F_Chopped=Archive_F;
Archive_mem_ranks_updated=Archive_mem_ranks;

% ----------------------------------------------------------------------- %

clear; close all; clc; % format long g

% Initial parameters of the MOAOS algorithm for j=8 max_iter=100; Pop=100; ArchiveMaxSize=100;

nvar=30; % obj_no=2;

method=3; if method==3

Archive_F1=load(sprintf('P%d.txt',j)); end obj_no=2; mkdir (sprintf('P%d',j)) %-------------------------- MOAOS----------------------------------------- for i=1% Numbver of independent runs

[Archive_F]=MAOS(max_iter,ArchiveMaxSize,Pop,nvar,method,j); if numel(Archive_F')==2 continue end Archive_F=Archive_F';

if obj_no==2 plot(Archive_F1(:,1),Archive_F1(:,2),'Color','g','LineWidth',4); hold on plot(Archive_F(:,1),Archive_F(:,2),'ro','LineWidth',1,... 'MarkerEdgeColor','b',... 'MarkerFaceColor','r',... 'Marker','o',... 'MarkerSize',10); legend('True PF','Obtained PF'); title(sprintf('MOAOS FOR MMF%d PROBLEM',j)); xlabel('obj_1'); ylabel('obj_2');

hold off end if obj_no==3 plot3(Archive_F1(:,1),Archive_F1(:,2),Archive_F1(:,3),'Color','g','LineWidth',1); hold on plot3(Archive_F(:,1),Archive_F(:,2),Archive_F(:,3),'ro','LineWidth',1,... 'MarkerEdgeColor','b',... 'MarkerFaceColor','r',... 'Marker','o',... 'MarkerSize',10); legend('True PF','Obtained PF'); title(sprintf('MOAOS FOR MMF%d PROBLEM',j)); xlabel('obj_1'); ylabel('obj_2'); zlabel('obj_3');

hold off end

savefig(sprintf('P%d/fig_%d.fig',j,i));

end

save(sprintf('P%d/result_P%d.mat',j,j));

end

3 运行结果

4 参考文献

Abualigah, L., Yousri, D., Elaziz, M.A., Ewees, A.A., A. Al-qaness, M.A., Gandomi,A.H., Aquila Optimizer: A novel meta-heuristic optimization Algorithm, Computers & Industrial Engineering
(2021)

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