【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码

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matlab科研助手 2022-02-04 12:09:13 博主文章分类：优化求解 ©著作权

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1 简介

蚁狮算法( ALO) 通过对随机解的探索，逐渐寻找近似最优解。与粒子群等优化算法不同的是，在 ALO 算法中有两类种群个体，即蚂蚁和蚁狮。ALO 算法的寻优过程模拟蚁狮捕食的六个基本步骤: 蚂蚁的随机移动、蚁狮建立陷阱、蚂蚁落入陷阱、蚂蚁滑向蚁狮、蚁狮捕捉蚂蚁和重建陷阱。ALO 算法的基本步骤和数学模型如下:

【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码_优化算法

【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码_多目标_02

【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码_优化算法_03

【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码_优化算法_04

多目标蚁狮算法( Multi － objective ant lion optimizer， MOALO) 是用基本蚁狮算法求解

【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码_hive_05

多目标问题。在单目标优化问题中，只需选择适应度函数最优的个体。而在多目标优化中，需要计算多个适应度函数的值，并通过 Pareto 支配关系选择最优解，放入外部存档中。【智能优化算法】基于蚁狮算法求解多目标问题附Matlab代码_hive_06

2 部分代码

%_________________________________________________________________________%

% Multi-Objective Ant Lion Optimizer (MALO) source codes demo %

% version 1.0 %

% %

%_________________________________________________________________________%

clc;

clear;

close all;

% Change these details with respect to your problem%%%%%%%%%%%%%%

ObjectiveFunction=@ZDT1;

dim=5;

lb=0;

ub=1;

obj_no=2;

if size(ub,2)==1

ub=ones(1,dim)*ub;

lb=ones(1,dim)*lb;

end

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

% Initial parameters of the MODA algorithm

max_iter=100;

N=100;

ArchiveMaxSize=100;

Archive_X=zeros(100,dim);

Archive_F=ones(100,obj_no)*inf;

Archive_member_no=0;

r=(ub-lb)/2;

V_max=(ub(1)-lb(1))/10;

Elite_fitness=inf*ones(1,obj_no);

Elite_position=zeros(dim,1);

Ant_Position=initialization(N,dim,ub,lb);

fitness=zeros(N,2);

V=initialization(N,dim,ub,lb);

iter=0;

position_history=zeros(N,max_iter,dim);

for iter=1:max_iter

for i=1:N %Calculate all the objective values first

Particles_F(i,:)=ObjectiveFunction(Ant_Position(:,i)');

if dominates(Particles_F(i,:),Elite_fitness)

Elite_fitness=Particles_F(i,:);

Elite_position=Ant_Position(:,i);

end

[Archive_X, Archive_F, Archive_member_no]=UpdateArchive(Archive_X, Archive_F, Ant_Position, Particles_F, Archive_member_no);

if Archive_member_no>ArchiveMaxSize

Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no);

[Archive_X, Archive_F, Archive_mem_ranks, Archive_member_no]=HandleFullArchive(Archive_X, Archive_F, Archive_member_no, Archive_mem_ranks, ArchiveMaxSize);

else

Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no);

end

Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no);

% Chose the archive member in the least population area as arrtactor

% to improve coverage

index=RouletteWheelSelection(1./Archive_mem_ranks);

if index==-1

index=1;

end

Elite_fitness=Archive_F(index,:);

Elite_position=Archive_X(index,:)';