【智能优化算法-人工蜂鸟算法】基于人工蜂鸟算法求解多目标优化问题附matlab代码MOAHA

原创已于 2022-08-02 16:14:10 修改 · 777 阅读

1 ·

CC 4.0 BY-SA版权

文章标签：

#matlab #算法 #开发语言

于 2022-07-30 09:42:15 首次发布

优化求解专栏收录该内容

553 篇文章

订阅专栏

该博客介绍了人工蜂鸟算法（AHA），一种用于解决优化问题的新型生物启发式算法。AHA模仿蜂鸟的飞行技巧和觅食策略，通过轴向、对角线和全向飞行模拟，以及引导、领地和迁徙觅食策略。通过数值测试和与现有算法的比较，证明AHA在找到高质量解决方案方面具有竞争力，且计算负担轻。此外，AHA在十个工程设计案例中表现出色，优于其他优化技术。博客提供了多目标人工蜂鸟算法（MOAHA）的MATLAB代码示例，并展示了在ZDT系列函数上的运行结果。

1 内容介绍

在这项工作中提出了一种新的仿生优化算法，称为人工蜂鸟算法（AHA）来解决优化问题。 AHA算法模拟自然界中蜂鸟的特殊飞行技能和智能觅食策略。模拟了觅食策略中使用的三种飞行技能，包括轴向、对角线和全向飞行。此外，实施了引导觅食、领地觅食和迁徙觅食，并构建了访问表来模拟蜂鸟对食物来源的记忆功能。 AHA 使用两组数值测试函数进行验证，并将结果与从各种算法获得的结果进行比较。比较表明，AHA 比其他元启发式算法更具竞争力，并且可以用更少的控制参数确定高质量的解决方案。此外，AHA 的性能在十个具有挑战性的工程设计案例研究中得到验证。结果表明，与文献中现有的优化技术相比，AHA 在计算负担和求解精度方面的优越性。

2 仿真代码

%-------------------------------------------------------------------% % Multi-Objective artificial hummingbird algorithm (MOAHA) % % Source codes demo version 1.0 % %-------------------------------------------------------------------% % I acknowledge that this version of MOAHA has been written using % a portion of the following code: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % MATLAB Code for % % % % Multi-Objective Particle Swarm Optimization (MOPSO) % % Version 1.0 - Feb. 2011 % % % % According to: % % Carlos A. Coello Coello et al., % % "Handling Multiple Objectives with Particle Swarm Optimization," % % IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, % % pp. 256-279, June 2004. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function dom=Dominates(x,y) dom=all(x<=y) && any(x<y);

end

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

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function [pop, F]=NonDominatedSorting(pop)

nPop=numel(pop);

for i=1:nPop
pop(i).DominationSet=[];
pop(i).DominatedCount=0;
end

F{1}=[];

for i=1:nPop
for j=i+1:nPop
p=pop(i);
q=pop(j);

if Dominates(p.Cost,q.Cost)
p.DominationSet=[p.DominationSet j];
q.DominatedCount=q.DominatedCount+1;
end

if Dominates(q.Cost,p.Cost)
q.DominationSet=[q.DominationSet i];
p.DominatedCount=p.DominatedCount+1;
end

pop(i)=p;
pop(j)=q;
end

if pop(i).DominatedCount==0
F{1}=[F{1} i];
pop(i).Rank=1;
end
end

k=1;

while true

Q=[];

for i=F{k}
p=pop(i);

for j=p.DominationSet
q=pop(j);

q.DominatedCount=q.DominatedCount-1;

if q.DominatedCount==0
Q=[Q j];
q.Rank=k+1;
end

pop(j)=q;
end
end

if isempty(Q)
break;
end

F{k+1}=Q;

k=k+1;

end

end

%--------------------------------------------------------------------------% % Multi-Objective artificial hummingbird algorithm (MOAHA) % % Source codes demo version 1.0 % ------------------------------------------------------------% % FunIndex = 1: ZDT1 % FunIndex = 2: ZDT2 % FunIndex = 3: ZDT3 % FunIndex = 4: ZDT4 % FunIndex = 5: ZDT6

clc; clear; MaxIt=300; nPop=100; Dim=30; ArchiveSize=100;

FunIndex=1;% ZDT1

[ArchiveFit]=MOAHA(FunIndex,MaxIt,nPop,ArchiveSize); figure; plot(ArchiveFit(1,:),ArchiveFit(2,:),'r.'); if FunIndex==5 title(['ZDT',num2str(FunIndex+1)]); else title(['ZDT',num2str(FunIndex)]); end xlabel('f1'); ylabel('f2'); box on legend('PF');

3 运行结果

4 参考文献

[1]刘明辉. 改进的多目标人工蜂群算法及其在特征选择中的应用[D]. 安徽大学, 2018.

[2] Zhao W , Wang L , Mirjalili S . Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 388:114194-.