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Publication
Particle swarm optimisation: a historical review up to the current developments
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Advisor(s)
Abstract(s)
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.
Description
Keywords
Particle Swarm Optimisation (PSO) Swarm intelligence Computational intelligence Bio-inspired algorithms Stochastic algorithms Optimisation . Faculdade de Ciências Exatas e da Engenharia
Citation
Freitas, D., Lopes, L. G., & Morgado-Dias, F. (2020). Particle swarm optimisation: a historical review up to the current developments. Entropy, 22(3), 362. https://doi.org/10.3390/e22030362
Publisher
MDPI