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      A Comprehensive Survey on Aquila Optimizer

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          Abstract

          Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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          Genetic Algorithms

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            The Arithmetic Optimization Algorithm

            Laith M. Abualigah, Ali Diabat, Seyedali Mirjalili … (2021)
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              Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

              Seyedali Mirjalili (2016)
              Article 0 comments Cited 532 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Buddhadev Sasmal: buddhadev.sasmal@midnaporecollege.ac.in
                Abdelazim G. Hussien: aga08@fayoum.edu.eg
                Arunita Das: arunita.das@midnaporecollege.ac.in
                Krishna Gopal Dhal:
                ORCID: http://orcid.org/0000-0002-6748-0569
                krishnagopal.dhal@midnaporecollege.ac.in
                Journal
                Journal ID (nlm-ta): Arch Comput Methods Eng
                Journal ID (iso-abbrev): Arch Comput Methods Eng
                Title: Archives of Computational Methods in Engineering
                Publisher: Springer Netherlands (Dordrecht )
                ISSN (Print): 1134-3060
                ISSN (Electronic): 1886-1784
                Publication date (Electronic): 7 June 2023
                Pages: 1-28
                Affiliations
                [1 ]GRID grid.412834.8, ISNI 0000 0000 9152 1805, Department of Computer Science and Application, , Midnapore College (Autonomous), ; Paschim Medinipur, West Bengal India
                [2 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Department of Computer and Information Science, , Linköping University, ; 58183 Linköping, Sweden
                [3 ]GRID grid.411170.2, ISNI 0000 0004 0412 4537, Faculty of Science, , Fayoum University, ; Fayoum, Egypt
                [4 ]GRID grid.449114.d, ISNI 0000 0004 0457 5303, MEU Research Unit, , Middle East University, ; Amman, Jordan
                Author information
                http://orcid.org/0000-0002-6748-0569
                Article
                Publisher ID: 9945
                DOI: 10.1007/s11831-023-09945-6
                PMC ID: 10245365
                SO-VID: a152fe55-e1a8-4bdb-b4cd-0d28c6ec3750
                Copyright © © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 12 April 2023
                Date accepted : 17 May 2023
                Categories
                Subject: Survey Article

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