Particle Swarm Optimization: A Comprehensive Survey

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a la...

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Vydáno v:	IEEE access Ročník 10; s. 10031 - 10061
Hlavní autoři:	Shami, Tareq M., El-Saleh, Ayman A., Alswaitti, Mohammed, Al-Tashi, Qasem, Summakieh, Mhd Amen, Mirjalili, Seyedali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applications of PSO binary PSO Birds Evolutionary algorithms Evolutionary computation Feature extraction feature selection Genetic algorithms Heuristic methods hybrid algorithms meta-heuristic algorithms Optimization Parameter modification Particle swarm optimization PSO variants Signal processing algorithms Statistics Topology
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.
AbstractList	Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.
Author	Summakieh, Mhd Amen Al-Tashi, Qasem El-Saleh, Ayman A. Alswaitti, Mohammed Mirjalili, Seyedali Shami, Tareq M.
Author_xml	– sequence: 1 givenname: Tareq M. orcidid: 0000-0002-0159-4985 surname: Shami fullname: Shami, Tareq M. organization: Department of Electronic Engineering, University of York, York, U.K – sequence: 2 givenname: Ayman A. orcidid: 0000-0003-4754-9271 surname: El-Saleh fullname: El-Saleh, Ayman A. organization: Department of Electronics and Communication Engineering, College of Engineering, A'Sharqiyah University, Ibra, Oman – sequence: 3 givenname: Mohammed orcidid: 0000-0003-0580-6954 surname: Alswaitti fullname: Alswaitti, Mohammed email: alswaitti.mohammed@xmu.edu.my organization: Department of Information and Communication Technology, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, Malaysia – sequence: 4 givenname: Qasem orcidid: 0000-0001-7208-693X surname: Al-Tashi fullname: Al-Tashi, Qasem organization: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA – sequence: 5 givenname: Mhd Amen orcidid: 0000-0002-5789-0337 surname: Summakieh fullname: Summakieh, Mhd Amen organization: Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia – sequence: 6 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD, Australia
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Snippet	Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good...
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SubjectTerms	Algorithms Applications of PSO binary PSO Birds Evolutionary algorithms Evolutionary computation Feature extraction feature selection Genetic algorithms Heuristic methods hybrid algorithms meta-heuristic algorithms Optimization Parameter modification Particle swarm optimization PSO variants Signal processing algorithms Statistics Topology
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Title	Particle Swarm Optimization: A Comprehensive Survey
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