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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Vydané v:IEEE access Ročník 10; s. 10031 - 10061
Hlavní autori: Shami, Tareq M., El-Saleh, Ayman A., Alswaitti, Mohammed, Al-Tashi, Qasem, Summakieh, Mhd Amen, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3142859