Aquila Optimizer: A novel meta-heuristic optimization algorithm

Abualigah, L; Yousri, D; Abd Elaziz, M; Ewees, AA; Al-qaness, MAA; Gandomi, AH

Aquila Optimizer: A novel meta-heuristic optimization algorithm

Abualigah, L Yousri, D Abd Elaziz, M Ewees, AA Al-qaness, MAA Gandomi, AH

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Computers and Industrial Engineering, 2021, 157
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Abualigah, L
dc.contributor.author	Yousri, D
dc.contributor.author	Abd Elaziz, M
dc.contributor.author	Ewees, AA
dc.contributor.author	Al-qaness, MAA
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-03-17T21:32:47Z
dc.date.available	2022-03-17T21:32:47Z
dc.date.issued	2021-07-01
dc.identifier.citation	Computers and Industrial Engineering, 2021, 157
dc.identifier.issn	0360-8352
dc.identifier.issn	1879-0550
dc.identifier.uri	http://hdl.handle.net/10453/155300
dc.description.abstract	This paper proposes a novel population-based optimization method, called Aquila Optimizer (AO), which is inspired by the Aquila's behaviors in nature during the process of catching the prey. Hence, the optimization procedures of the proposed AO algorithm are represented in four methods; selecting the search space by high soar with the vertical stoop, exploring within a diverge search space by contour flight with short glide attack, exploiting within a converge search space by low flight with slow descent attack, and swooping by walk and grab prey. To validate the new optimizer's ability to find the optimal solution for different optimization problems, a set of experimental series is conducted. For example, during the first experiment, AO is applied to find the solution of well-known 23 functions. The second and third experimental series aims to evaluate the AO's performance to find solutions for more complex problems such as thirty CEC2017 test functions and ten CEC2019 test functions, respectively. Finally, a set of seven real-world engineering problems are used. From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed. Matlab codes of AO are available at https://www.mathworks.com/matlabcentral/fileexchange/89381-aquila-optimizer-a-meta-heuristic-optimization-algorithm and Java codes are available at https://www.mathworks.com/matlabcentral/fileexchange/89386-aquila-optimizer-a-meta-heuristic-optimization-algorithm.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Computers and Industrial Engineering
dc.relation.isbasedon	10.1016/j.cie.2021.107250
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Aquila Optimizer: A novel meta-heuristic optimization algorithm
dc.type	Journal Article
utslib.citation.volume	157
utslib.location.activity	Sakarya, TURKEY
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-17T21:32:37Z
pubs.publication-status	Published
pubs.volume	157

Abstract:

This paper proposes a novel population-based optimization method, called Aquila Optimizer (AO), which is inspired by the Aquila's behaviors in nature during the process of catching the prey. Hence, the optimization procedures of the proposed AO algorithm are represented in four methods; selecting the search space by high soar with the vertical stoop, exploring within a diverge search space by contour flight with short glide attack, exploiting within a converge search space by low flight with slow descent attack, and swooping by walk and grab prey. To validate the new optimizer's ability to find the optimal solution for different optimization problems, a set of experimental series is conducted. For example, during the first experiment, AO is applied to find the solution of well-known 23 functions. The second and third experimental series aims to evaluate the AO's performance to find solutions for more complex problems such as thirty CEC2017 test functions and ten CEC2019 test functions, respectively. Finally, a set of seven real-world engineering problems are used. From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed. Matlab codes of AO are available at https://www.mathworks.com/matlabcentral/fileexchange/89381-aquila-optimizer-a-meta-heuristic-optimization-algorithm and Java codes are available at https://www.mathworks.com/matlabcentral/fileexchange/89386-aquila-optimizer-a-meta-heuristic-optimization-algorithm.

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