Particle Swarms Reformulated towards a Unified and Flexible Framework

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The Particle Swarm Optimisation (PSO) algorithm has undergone countless modifications and adaptations since its original formulation in 1995. Some of these have become mainstream whereas others have faded away. A myriad of alternative formulations have been proposed raising the question of what the basic features of an algorithm must be to belong in the PSO family. The aim of this paper is to establish what defines a PSO algorithm and to attempt to formulate it in such a way that it encompasses many existing variants. Therefore, different versions of the method may be posed as settings within the proposed unified framework. In addition, the proposed formulation generalises, decouples and incorporates features to the method providing more flexibility to the behaviour of each particle. The closed forms of the trajectory difference equation are obtained, different types of behaviour are identified, stochasticity is decoupled, and traditionally global features such as sociometries and constraint-handling are re-defined as particle’s attributes.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence
Subtitle of host publication12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I
EditorsYing Tan, Yuhui Shi
PublisherSpringer Nature
Number of pages12
ISBN (Electronic)978-3-030-78743-1
ISBN (Print)978-3-030-78742-4
Publication statusPublished - 2021
EventTwelfth International Conference on Swarm Intelligence - Virtual presentation permitted, Qingdao, China
Duration: 17 Jul 202121 Jul 2021
Conference number: 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12689 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceTwelfth International Conference on Swarm Intelligence
Abbreviated titleICSI 2021
Internet address

Bibliographical note

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  • Particle Swarm Optimisation
  • Coefficients’ settings
  • Types of behaviour
  • Trajectory
  • Learning strategy
  • Unstructured neighbourhood


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