Particle Swarm Optimisation—A Historical Review Up to the Current Developments
Abstract
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.
More information
- Authors
- Diogo Freitas; Luiz Guerreiro Lopes; Fernando Morgado-Dias
- Date
- 2020
- Journal
- Entropy
- Special issue
- Intelligent Tools and Applications in Engineering and Mathematics
- Publisher
- MDPI
- Source
- Link
Citation
@article{freitas2020,
author = {Freitas, Diogo and Lopes, Luiz Guerreiro and Morgado-Dias, Fernando},
doi = {10.3390/e22030362},
journal = {Entropy},
month = {3},
number = {3},
pages = {362},
title = {Particle swarm optimisation: A historical review up to the current developments},
volume = {22},
year = {2020}
}
