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Artificial neural networks based optimization techniques: A review

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e...

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Main Authors:	Abdolrasol M.G.M., Suhail Hussain S.M., Ustun T.S., Sarker M.R., Hannan M.A., Mohamed R., Ali J.A., Mekhilef S., Milad A.
Other Authors:	35796848700
Format:	Review
Published:	MDPI 2023
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id	my.uniten.dspace-25927
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spelling	my.uniten.dspace-259272023-05-29T17:05:35Z Artificial neural networks based optimization techniques: A review Abdolrasol M.G.M. Suhail Hussain S.M. Ustun T.S. Sarker M.R. Hannan M.A. Mohamed R. Ali J.A. Mekhilef S. Milad A. 35796848700 22035146400 43761679200 57537703000 7103014445 7005169066 56540826800 57928298500 57189499179 In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system. � 2021 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T09:05:35Z 2023-05-29T09:05:35Z 2021 Review 10.3390/electronics10212689 2-s2.0-85118328878 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118328878&doi=10.3390%2felectronics10212689&partnerID=40&md5=fa80379df58489d2a4ee06eb8a6d98dd https://irepository.uniten.edu.my/handle/123456789/25927 10 21 2689 All Open Access, Gold MDPI 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/
description	In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.
author2	35796848700
author_facet	35796848700 Abdolrasol M.G.M. Suhail Hussain S.M. Ustun T.S. Sarker M.R. Hannan M.A. Mohamed R. Ali J.A. Mekhilef S. Milad A.
format	Review
author	Abdolrasol M.G.M. Suhail Hussain S.M. Ustun T.S. Sarker M.R. Hannan M.A. Mohamed R. Ali J.A. Mekhilef S. Milad A.
spellingShingle	Abdolrasol M.G.M. Suhail Hussain S.M. Ustun T.S. Sarker M.R. Hannan M.A. Mohamed R. Ali J.A. Mekhilef S. Milad A. Artificial neural networks based optimization techniques: A review
author_sort	Abdolrasol M.G.M.
title	Artificial neural networks based optimization techniques: A review
title_short	Artificial neural networks based optimization techniques: A review
title_full	Artificial neural networks based optimization techniques: A review
title_fullStr	Artificial neural networks based optimization techniques: A review
title_full_unstemmed	Artificial neural networks based optimization techniques: A review
title_sort	artificial neural networks based optimization techniques: a review
publisher	MDPI
publishDate	2023
_version_	1806427932519825408
score	13.222552

Artificial neural networks based optimization techniques: A review

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