A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing

Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned te...

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Autores principales:	Allahkaram Shafiei, Mohammad (Behdad) Jamshidi, Farzad Khani, Jakub Talla, Zdenêk Peroutka, Rahma Gantassi, Mohammed Baz, Omar Cheikhrouhou, Habib Hamam
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/4da4592b739e4950b12e0d708561c9f4
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spelling	oai:doaj.org-article:4da4592b739e4950b12e0d708561c9f42021-11-08T02:36:23ZA Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing1563-514710.1155/2021/9194578https://doaj.org/article/4da4592b739e4950b12e0d708561c9f42021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9194578https://doaj.org/toc/1563-5147Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with s-norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.Allahkaram ShafieiMohammad (Behdad) JamshidiFarzad KhaniJakub TallaZdenêk PeroutkaRahma GantassiMohammed BazOmar CheikhrouhouHabib HamamHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Allahkaram Shafiei Mohammad (Behdad) Jamshidi Farzad Khani Jakub Talla Zdenêk Peroutka Rahma Gantassi Mohammed Baz Omar Cheikhrouhou Habib Hamam A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
description	Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with s-norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.
format	article
author	Allahkaram Shafiei Mohammad (Behdad) Jamshidi Farzad Khani Jakub Talla Zdenêk Peroutka Rahma Gantassi Mohammed Baz Omar Cheikhrouhou Habib Hamam
author_facet	Allahkaram Shafiei Mohammad (Behdad) Jamshidi Farzad Khani Jakub Talla Zdenêk Peroutka Rahma Gantassi Mohammed Baz Omar Cheikhrouhou Habib Hamam
author_sort	Allahkaram Shafiei
title	A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
title_short	A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
title_full	A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
title_fullStr	A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
title_full_unstemmed	A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
title_sort	hybrid technique based on a genetic algorithm for fuzzy multiobjective problems in 5g, internet of things, and mobile edge computing
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4da4592b739e4950b12e0d708561c9f4
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A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing

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