Harris hawks optimization: Algorithm and applications

被引:4391
作者
Heidari, Ali Asghar [1 ,2 ]
Mirjalili, Seyedali [3 ]
Faris, Hossam [4 ]
Aljarah, Ibrahim [4 ]
Mafarja, Majdi [5 ]
Chen, Huiling [6 ]
机构
[1] Univ Tehran, Sch Surveying & Geospatial Engn, Tehran, Iran
[2] Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore, Singapore
[3] Griffith Univ, Sch Informat & Commun Technol, Brisbane, Qld 4111, Australia
[4] Univ Jordan, King Abdullah II Sch Informat Technol, Amman, Jordan
[5] Birzeit Univ, Dept Comp Sci, POB 14, West Bank, Palestine
[6] Wenzhou Univ, Dept Comp Sci, Wenzhou 325035, Peoples R China
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 97卷
关键词
Nature-inspired computing; Harris hawks optimization algorithm; Swarm intelligence; Optimization; Metaheuristic; PARTICLE SWARM OPTIMIZATION; ENGINEERING OPTIMIZATION; STRUCTURAL OPTIMIZATION; DIFFERENTIAL EVOLUTION; DESIGN OPTIMIZATION; GLOBAL OPTIMIZATION; LEVY FLIGHTS; SEARCH; SELECTION; IDENTIFICATION;
D O I
10.1016/j.future.2019.02.028
中图分类号
TP301 [理论、方法];
学科分类号
080201 [机械制造及其自动化];
摘要
In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO). The main inspiration of HHO is the cooperative behavior and chasing style of Harris' hawks in nature called surprise pounce. In this intelligent strategy, several hawks cooperatively pounce a prey from different directions in an attempt to surprise it. Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the prey. This work mathematically mimics such dynamic patterns and behaviors to develop an optimization algorithm. The effectiveness of the proposed HHO optimizer is checked, through a comparison with other nature-inspired techniques, on 29 benchmark problems and several real-world engineering problems. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques. Source codes of HHO are publicly available at http://www.alimirjalili.com/HHO.html and http://www.evo-ml.com/2019/03/02/hho. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:849 / 872
页数:24
相关论文
共 95 条
[1]
A. A. S. EurekAlertA, 2005, BIRD IQ TEST TAK FLI
[2]
An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models [J].
Abbassi, Rabeh ;
Abbassi, Abdelkader ;
Heidari, Ali Asghar ;
Mirjalili, Seyedali .
ENERGY CONVERSION AND MANAGEMENT, 2019, 179 :362-372
[3]
Asynchronous accelerating multi-leader salp chains for feature selection [J].
Aljarah, Ibrahim ;
Mafarja, Majdi ;
Heidari, Ali Asghar ;
Faris, Hossam ;
Zhang, Yong ;
Mirjalili, Seyedali .
APPLIED SOFT COMPUTING, 2018, 71 :964-979
[4]
ARORA JS, 1967, INTRO OPTIMUM DESIGN
[5]
Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization [J].
Baykasoglu, Adil ;
Ozsoydan, Fehmi B. .
INFORMATION SCIENCES, 2017, 420 :159-183
[6]
COOPERATIVE HUNTING IN HARRIS HAWKS (PARABUTEO-UNICINCTUS) [J].
BEDNARZ, JC .
SCIENCE, 1988, 239 (4847) :1525-1527
[7]
A STUDY OF MATHEMATICAL-PROGRAMMING METHODS FOR STRUCTURAL OPTIMIZATION .1. THEORY [J].
BELEGUNDU, AD ;
ARORA, JS .
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 1985, 21 (09) :1583-1599
[8]
Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems [J].
Coelho, Leandro dos Santos .
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (02) :1676-1683
[9]
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection [J].
Coello, CAC ;
Montes, EM .
ADVANCED ENGINEERING INFORMATICS, 2002, 16 (03) :193-203
[10]
Use of a self-adaptive penalty approach for engineering optimization problems [J].
Coello, CAC .
COMPUTERS IN INDUSTRY, 2000, 41 (02) :113-127