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Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear n...

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Main Authors:	Hosseini E., Al-Ghaili A.M., Kadir D.H., Gunasekaran S.S., Ahmed A.N., Jamil N., Deveci M., Razali R.A.
Other Authors:	57212521533
Format:	Review
Published:	Elsevier Ltd 2025
Subjects:	Clustering algorithms Heuristic algorithms Learning algorithms Learning systems Optimization Deep learning Energy Energy applications Excel Meta-heuristics algorithms Metaheuristic Modeling and forecasting Optimality Renewable energies Research challenges
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id	my.uniten.dspace-36584
record_format	dspace
spelling	my.uniten.dspace-365842025-03-03T15:43:13Z Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023) Hosseini E. Al-Ghaili A.M. Kadir D.H. Gunasekaran S.S. Ahmed A.N. Jamil N. Deveci M. Razali R.A. 57212521533 26664381500 57211243421 55652730500 57214837520 36682671900 55734383000 35146685400 Clustering algorithms Heuristic algorithms Learning algorithms Learning systems Optimization Deep learning Energy Energy applications Excel Meta-heuristics algorithms Metaheuristic Modeling and forecasting Optimality Renewable energies Research challenges Deep learning The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include: 1. Overview of recent advancements in MHs, DL, and integration. 2. Coverage of trends from 2018 to 2023. 3. Introduction of Alpha metric for performance evaluation. 4. Innovative framework harmonizing MHs with DL for energy problems. ? 2024 Final 2025-03-03T07:43:13Z 2025-03-03T07:43:13Z 2024 Review 10.1016/j.esr.2024.101409 2-s2.0-85193900930 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193900930&doi=10.1016%2fj.esr.2024.101409&partnerID=40&md5=00ee3b19e8f7eb43dcdfe6e9105a2011 https://irepository.uniten.edu.my/handle/123456789/36584 53 101409 All Open Access; Gold Open Access Elsevier Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Clustering algorithms Heuristic algorithms Learning algorithms Learning systems Optimization Deep learning Energy Energy applications Excel Meta-heuristics algorithms Metaheuristic Modeling and forecasting Optimality Renewable energies Research challenges Deep learning
spellingShingle	Clustering algorithms Heuristic algorithms Learning algorithms Learning systems Optimization Deep learning Energy Energy applications Excel Meta-heuristics algorithms Metaheuristic Modeling and forecasting Optimality Renewable energies Research challenges Deep learning Hosseini E. Al-Ghaili A.M. Kadir D.H. Gunasekaran S.S. Ahmed A.N. Jamil N. Deveci M. Razali R.A. Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
description	The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include: 1. Overview of recent advancements in MHs, DL, and integration. 2. Coverage of trends from 2018 to 2023. 3. Introduction of Alpha metric for performance evaluation. 4. Innovative framework harmonizing MHs with DL for energy problems. ? 2024
author2	57212521533
author_facet	57212521533 Hosseini E. Al-Ghaili A.M. Kadir D.H. Gunasekaran S.S. Ahmed A.N. Jamil N. Deveci M. Razali R.A.
format	Review
author	Hosseini E. Al-Ghaili A.M. Kadir D.H. Gunasekaran S.S. Ahmed A.N. Jamil N. Deveci M. Razali R.A.
author_sort	Hosseini E.
title	Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
title_short	Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
title_full	Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
title_fullStr	Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
title_full_unstemmed	Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
title_sort	meta-heuristics and deep learning for energy applications: review and open research challenges (2018?2023)
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816111446228992
score	13.244109

Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

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