Particle Swarms Reformulated towards a Unified and Flexible Framework

Mauro Innocente

doi:10.1007/978-3-030-78743-1_25

Particle Swarms Reformulated towards a Unified and Flexible Framework

Mauro Innocente

Centre for Future Transport and Cities

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Abstract

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 language	English
Title of host publication	Advances in Swarm Intelligence
Subtitle of host publication	12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I
Editors	Ying Tan, Yuhui Shi
Publisher	Springer Nature
Pages	275-286
Number of pages	12
Edition	1
ISBN (Electronic)	978-3-030-78743-1
ISBN (Print)	978-3-030-78742-4
DOIs	https://doi.org/10.1007/978-3-030-78743-1_25
Publication status	Published - 2021
Event	Twelfth International Conference on Swarm Intelligence - Virtual presentation permitted, Qingdao, China Duration: 17 Jul 2021 → 21 Jul 2021 Conference number: 12 http://www.iasei.org/icsi2021/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12689 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Twelfth International Conference on Swarm Intelligence
Abbreviated title	ICSI 2021
Country/Territory	China
City	Qingdao
Period	17/07/21 → 21/07/21
Internet address	http://www.iasei.org/icsi2021/

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-78743-1_25

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

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

Access to Document

10.1007/978-3-030-78743-1_25

Post-PrintAccepted author manuscript, 1.03 MBLicence: CC BY-NC-ND

Cite this

Innocente, M. (2021). Particle Swarms Reformulated towards a Unified and Flexible Framework. In Y. Tan, & Y. Shi (Eds.), Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I (1 ed., pp. 275-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12689 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-78743-1_25

Particle Swarms Reformulated towards a Unified and Flexible Framework. / Innocente, Mauro.
Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I. ed. / Ying Tan; Yuhui Shi. 1. ed. Springer Nature, 2021. p. 275-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12689 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Innocente, M 2021, Particle Swarms Reformulated towards a Unified and Flexible Framework. in Y Tan & Y Shi (eds), Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12689 LNCS, Springer Nature, pp. 275-286, Twelfth International Conference on Swarm Intelligence, Qingdao, China, 17/07/21. https://doi.org/10.1007/978-3-030-78743-1_25

Innocente M. Particle Swarms Reformulated towards a Unified and Flexible Framework. In Tan Y, Shi Y, editors, Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I. 1 ed. Springer Nature. 2021. p. 275-286. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2021 Jul 7. doi: 10.1007/978-3-030-78743-1_25

Innocente, Mauro. / Particle Swarms Reformulated towards a Unified and Flexible Framework. Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I. editor / Ying Tan ; Yuhui Shi. 1. ed. Springer Nature, 2021. pp. 275-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Particle Swarms Reformulated towards a Unified and Flexible Framework

Abstract

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Keywords

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