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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Autores principales:	Maher G. M. Abdolrasol, S. M. Suhail Hussain, Taha Selim Ustun, Mahidur R. Sarker, Mahammad A. Hannan, Ramizi Mohamed, Jamal Abd Ali, Saad Mekhilef, Abdalrhman Milad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial neural networks optimization algorithms machine learning ANN enhancement PSO BSA Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/fa83a4e40c30428694927a37ea2f3e92
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id	oai:doaj.org-article:fa83a4e40c30428694927a37ea2f3e92
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spelling	oai:doaj.org-article:fa83a4e40c30428694927a37ea2f3e922021-11-11T15:40:51ZArtificial Neural Networks Based Optimization Techniques: A Review10.3390/electronics102126892079-9292https://doaj.org/article/fa83a4e40c30428694927a37ea2f3e922021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2689https://doaj.org/toc/2079-9292In 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.Maher G. M. AbdolrasolS. M. Suhail HussainTaha Selim UstunMahidur R. SarkerMahammad A. HannanRamizi MohamedJamal Abd AliSaad MekhilefAbdalrhman MiladMDPI AGarticleartificial neural networksoptimization algorithmsmachine learningANN enhancementPSOBSAElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2689, p 2689 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks optimization algorithms machine learning ANN enhancement PSO BSA Electronics TK7800-8360
spellingShingle	artificial neural networks optimization algorithms machine learning ANN enhancement PSO BSA Electronics TK7800-8360 Maher G. M. Abdolrasol S. M. Suhail Hussain Taha Selim Ustun Mahidur R. Sarker Mahammad A. Hannan Ramizi Mohamed Jamal Abd Ali Saad Mekhilef Abdalrhman Milad Artificial Neural Networks Based Optimization Techniques: A Review
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.
format	article
author	Maher G. M. Abdolrasol S. M. Suhail Hussain Taha Selim Ustun Mahidur R. Sarker Mahammad A. Hannan Ramizi Mohamed Jamal Abd Ali Saad Mekhilef Abdalrhman Milad
author_facet	Maher G. M. Abdolrasol S. M. Suhail Hussain Taha Selim Ustun Mahidur R. Sarker Mahammad A. Hannan Ramizi Mohamed Jamal Abd Ali Saad Mekhilef Abdalrhman Milad
author_sort	Maher G. M. Abdolrasol
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 AG
publishDate	2021
url	https://doaj.org/article/fa83a4e40c30428694927a37ea2f3e92
work_keys_str_mv	AT mahergmabdolrasol artificialneuralnetworksbasedoptimizationtechniquesareview AT smsuhailhussain artificialneuralnetworksbasedoptimizationtechniquesareview AT tahaselimustun artificialneuralnetworksbasedoptimizationtechniquesareview AT mahidurrsarker artificialneuralnetworksbasedoptimizationtechniquesareview AT mahammadahannan artificialneuralnetworksbasedoptimizationtechniquesareview AT ramizimohamed artificialneuralnetworksbasedoptimizationtechniquesareview AT jamalabdali artificialneuralnetworksbasedoptimizationtechniquesareview AT saadmekhilef artificialneuralnetworksbasedoptimizationtechniquesareview AT abdalrhmanmilad artificialneuralnetworksbasedoptimizationtechniquesareview
_version_	1718434385813831680

Artificial Neural Networks Based Optimization Techniques: A Review

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