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Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Souheila Khalfi Orcid Logo, Fabio Caraffini Orcid Logo, Giovanni Iacca Orcid Logo

International Journal of Intelligent Systems, Volume: 2023, Pages: 1 - 32

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1155/2023/5708085

Abstract

In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of in...

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Published in: International Journal of Intelligent Systems
ISSN: 0884-8173 1098-111X
Published: Hindawi Limited 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64958
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first_indexed 2023-11-09T20:53:30Z
last_indexed 2023-11-09T20:53:30Z
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spelling v2 64958 2023-11-09 Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-11-09 SCS In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions. Journal Article International Journal of Intelligent Systems 2023 1 32 Hindawi Limited 0884-8173 1098-111X 4 11 2023 2023-11-04 10.1155/2023/5708085 http://dx.doi.org/10.1155/2023/5708085 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) 2023-12-05T15:37:39.0308314 2023-11-09T20:50:56.5993531 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Souheila Khalfi 0000-0002-5033-8937 1 Fabio Caraffini 0000-0001-9199-7368 2 Giovanni Iacca 0000-0001-9723-1830 3 64958__29213__464725ba53fd40b3be59b4faa0c463c6.pdf 64958.VOR.pdf 2023-12-05T15:32:02.0105367 Output 971016 application/pdf Version of Record true Copyright © 2023 Souheila Khalfi et al. Distributed under the terms of a Creative Commons Attribution 4.0 International License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
spellingShingle Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
Fabio Caraffini
title_short Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
title_full Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
title_fullStr Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
title_full_unstemmed Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
title_sort Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Souheila Khalfi
Fabio Caraffini
Giovanni Iacca
format Journal article
container_title International Journal of Intelligent Systems
container_volume 2023
container_start_page 1
publishDate 2023
institution Swansea University
issn 0884-8173
1098-111X
doi_str_mv 10.1155/2023/5708085
publisher Hindawi Limited
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1155/2023/5708085
document_store_str 1
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description In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.
published_date 2023-11-04T15:37:39Z
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