Particle Swarm Optimisation: A Historical Review Up to the Current Developments
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...
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doaj-e936913382394c53a2976333ef1d878e2020-11-25T03:31:06ZengMDPI AGEntropy1099-43002020-03-0122336210.3390/e22030362e22030362Particle Swarm Optimisation: A Historical Review Up to the Current DevelopmentsDiogo Freitas0Luiz Guerreiro Lopes1Fernando Morgado-Dias2Madeira Interactive Technologies Institute (ITI/LARSyS/M-ITI), 9020-105 Funchal, PortugalFaculty of Exact Sciences and Engineering, University of Madeira, Penteada Campus, 9020-105 Funchal, PortugalMadeira Interactive Technologies Institute (ITI/LARSyS/M-ITI), 9020-105 Funchal, PortugalThe 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.https://www.mdpi.com/1099-4300/22/3/362particle swarm optimisation (pso)swarm intelligencecomputational intelligencebio-inspired algorithmsstochastic algorithmsoptimisation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Diogo Freitas Luiz Guerreiro Lopes Fernando Morgado-Dias |
spellingShingle |
Diogo Freitas Luiz Guerreiro Lopes Fernando Morgado-Dias Particle Swarm Optimisation: A Historical Review Up to the Current Developments Entropy particle swarm optimisation (pso) swarm intelligence computational intelligence bio-inspired algorithms stochastic algorithms optimisation |
author_facet |
Diogo Freitas Luiz Guerreiro Lopes Fernando Morgado-Dias |
author_sort |
Diogo Freitas |
title |
Particle Swarm Optimisation: A Historical Review Up to the Current Developments |
title_short |
Particle Swarm Optimisation: A Historical Review Up to the Current Developments |
title_full |
Particle Swarm Optimisation: A Historical Review Up to the Current Developments |
title_fullStr |
Particle Swarm Optimisation: A Historical Review Up to the Current Developments |
title_full_unstemmed |
Particle Swarm Optimisation: A Historical Review Up to the Current Developments |
title_sort |
particle swarm optimisation: a historical review up to the current developments |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2020-03-01 |
description |
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. |
topic |
particle swarm optimisation (pso) swarm intelligence computational intelligence bio-inspired algorithms stochastic algorithms optimisation |
url |
https://www.mdpi.com/1099-4300/22/3/362 |
work_keys_str_mv |
AT diogofreitas particleswarmoptimisationahistoricalreviewuptothecurrentdevelopments AT luizguerreirolopes particleswarmoptimisationahistoricalreviewuptothecurrentdevelopments AT fernandomorgadodias particleswarmoptimisationahistoricalreviewuptothecurrentdevelopments |
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