Ant Lion Optimization: Variants, Hybrids, and Applications

被引：106

作者：

Assiri, Adel Saad ^{[1
]}

Hussien, Abdelazim G. ^{[2
]}

Amin, Mohamed ^{[3
]}

机构：

[1] King Khalid Univ, Management Informat Syst Dept, Coll Business, Abha 62529, Saudi Arabia

[2] Fayoum Univ, Fac Sci, Al Fayyum 63514, Egypt

[3] Menoufia Univ, Fac Sci, Menoufia 32511, Egypt

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Ant lion optimizer; antlion; ALO; swarm intelligence; SI; meta-heuristics; optimization; nature-inspired algorithms; META-HEURISTIC OPTIMIZATION; NUMERICAL FUNCTION OPTIMIZATION; RECURRENT NEURAL-NETWORK; ANTLION OPTIMIZER; INSPIRED ALGORITHM; PID CONTROLLER; METAHEURISTIC ALGORITHM; SEARCH ALGORITHM; DISPATCH PROBLEM; KRILL HERD;

D O I：

10.1109/ACCESS.2020.2990338

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & x0026; possible future directions that can be investigated.

引用

页码：77746 / 77764

页数：19

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