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Review

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

1
Madeira Interactive Technologies Institute (ITI/LARSyS/M-ITI), 9020-105 Funchal, Portugal
2
Faculty of Exact Sciences and Engineering, University of Madeira, Penteada Campus, 9020-105 Funchal, Portugal
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(3), 362; https://doi.org/10.3390/e22030362
Received: 31 January 2020 / Revised: 18 March 2020 / Accepted: 19 March 2020 / Published: 21 March 2020
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application. View Full-Text
Keywords: Particle Swarm Optimisation (PSO); swarm intelligence; computational intelligence; bio-inspired algorithms; stochastic algorithms; optimisation Particle Swarm Optimisation (PSO); swarm intelligence; computational intelligence; bio-inspired algorithms; stochastic algorithms; optimisation
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MDPI and ACS Style

Freitas, D.; Lopes, L.G.; Morgado-Dias, F. Particle Swarm Optimisation: A Historical Review Up to the Current Developments. Entropy 2020, 22, 362. https://doi.org/10.3390/e22030362

AMA Style

Freitas D, Lopes LG, Morgado-Dias F. Particle Swarm Optimisation: A Historical Review Up to the Current Developments. Entropy. 2020; 22(3):362. https://doi.org/10.3390/e22030362

Chicago/Turabian Style

Freitas, Diogo, Luiz G. Lopes, and Fernando Morgado-Dias. 2020. "Particle Swarm Optimisation: A Historical Review Up to the Current Developments" Entropy 22, no. 3: 362. https://doi.org/10.3390/e22030362

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