Harris hawks optimization: Algorithm and applications

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...

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Published in:	Future generation computer systems Vol. 97; pp. 849 - 872
Main Authors:	Heidari, Ali Asghar, Mirjalili, Seyedali, Faris, Hossam, Aljarah, Ibrahim, Mafarja, Majdi, Chen, Huiling
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.08.2019
Subjects:	Harris hawks optimization algorithm Metaheuristic Nature-inspired computing Optimization Swarm intelligence Metaheuristic Harris hawks optimization algorithm Swarm intelligence Nature-inspired computing Optimization
ISSN:	0167-739X, 1872-7115
Online Access:	Get full text
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Abstract	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. •A mathematical model is proposed to simulate the hunting behavior of Harris’ Hawks.•An optimization algorithm is proposed using the mathematical model.•The proposed HHO algorithm is tested on several benchmarks.•The performance of HHO is also examined on several engineering design problems.•The results show the merits of the HHO algorithm as compared to the existing algorithms.
AbstractList	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. •A mathematical model is proposed to simulate the hunting behavior of Harris’ Hawks.•An optimization algorithm is proposed using the mathematical model.•The proposed HHO algorithm is tested on several benchmarks.•The performance of HHO is also examined on several engineering design problems.•The results show the merits of the HHO algorithm as compared to the existing algorithms.
Author	Heidari, Ali Asghar Chen, Huiling Mafarja, Majdi Faris, Hossam Aljarah, Ibrahim Mirjalili, Seyedali
Author_xml	– sequence: 1 givenname: Ali Asghar surname: Heidari fullname: Heidari, Ali Asghar email: as_heidari@ut.ac.ir, aliasgha@comp.nus.edu.sg, t0917038@u.nus.edu organization: School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran – sequence: 2 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali email: seyedali.mirjalili@griffithuni.edu.au organization: School of Information and Communication Technology, Griffith University, Nathan, Brisbane, QLD 4111, Australia – sequence: 3 givenname: Hossam surname: Faris fullname: Faris, Hossam email: hossam.faris@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 4 givenname: Ibrahim orcidid: 0000-0002-9265-9819 surname: Aljarah fullname: Aljarah, Ibrahim email: i.aljarah@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 5 givenname: Majdi surname: Mafarja fullname: Mafarja, Majdi email: mmafarja@birzeit.edu organization: Department of Computer Science, Birzeit University, POBox 14, West Bank, Palestine – sequence: 6 givenname: Huiling surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com, chenhuiling_jsj@wzu.edu.cn organization: Department of Computer Science, Wenzhou University, Wenzhou 325035, China
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Snippet	In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO). The main inspiration...
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SubjectTerms	Harris hawks optimization algorithm Metaheuristic Nature-inspired computing Optimization Swarm intelligence
Title	Harris hawks optimization: Algorithm and applications
URI	https://dx.doi.org/10.1016/j.future.2019.02.028
Volume	97
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