Article

Exposing origin-seeking bias in PSO

Authors:

Christopher K. Monson,

Kevin D. SeppiAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 241 - 248

https://doi.org/10.1145/1068009.1068045

Published: 25 June 2005 Publication History

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Abstract

We discuss testing methods for exposing origin-seeking bias in PSO motion algorithms. The strategy of resizing the initialization space, proposed by Gehlhaar and Fogel and made popular in the PSO context by Angeline, is shown to be insufficiently general for revealing an algorithm's tendency to focus its efforts on regions at or near the origin. An alternative testing method is proposed that reveals problems with PSO motion algorithms that are not visible when merely resizing the initialization space.

References

[1]

P. J. Angeline. Using selection to improve particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska, USA, 1998.

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Google Scholar

[2]

M. Clerc. TRIBES - un exemple d'optimisation par essaim particulaire sans paramètres de contrôle. In Optimisation par Essaim Particulaire (OEP 2003), Paris, France, 2003.

Google Scholar

[3]

M. Clerc. Math stuff about PSO. Online at http://clerc.maurice.free.fr/pso/, 2005. Repository of papers and source code for PSO algorithms including TRIBES.

Google Scholar

[4]

M. Clerc and J. Kennedy. The particle swarm: Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1):58--73, February 2002.

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Google Scholar

[5]

D. K. Gehlhaar and D. B. Fogel. Tuning evolutionary programming for conformationally flexible molecular docking. In Evolutionary Programming, pages 419--429, 1996.

Google Scholar

[6]

J. Kennedy. Bare bones particle swarms. In Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), pages 80--87, Indianapolis, Indiana, 2003.

Crossref

Google Scholar

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Rajwar KDeep K(2025)Structural bias in metaheuristic algorithms: Insights, open problems, and future prospectsSwarm and Evolutionary Computation10.1016/j.swevo.2024.10181292(101812)Online publication date: Feb-2025
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Rajwar KDeep K(2024)Uncovering structural bias in population-based optimization algorithms: A theoretical and simulation-based analysis of the Generalized Signature TestExpert Systems with Applications10.1016/j.eswa.2023.122332240(122332)Online publication date: Apr-2024
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https://dl.acm.org/doi/10.1016/j.ins.2019.05.019
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Index Terms

Exposing origin-seeking bias in PSO
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 June 2005

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June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Rajwar KDeep K(2025)Structural bias in metaheuristic algorithms: Insights, open problems, and future prospectsSwarm and Evolutionary Computation10.1016/j.swevo.2024.10181292(101812)Online publication date: Feb-2025
https://doi.org/10.1016/j.swevo.2024.101812
Rajwar KDeep K(2024)Uncovering structural bias in population-based optimization algorithms: A theoretical and simulation-based analysis of the Generalized Signature TestExpert Systems with Applications10.1016/j.eswa.2023.122332240(122332)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122332
Caraffini FKononova ACorne D(2019)Infeasibility and structural bias in differential evolutionInformation Sciences: an International Journal10.1016/j.ins.2019.05.019496:C(161-179)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.ins.2019.05.019
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Espitia HSofrony J(2018)Statistical analysis for vortex particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2018.03.00267:C(370-386)Online publication date: 1-Jun-2018
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Burman RChakrabarti SDas S(2017)Democracy-inspired particle swarm optimizer with the concept of peer groupsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-2007-821:12(3267-3286)Online publication date: 1-Jun-2017
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CAPSO: Centripetal accelerated particle swarm optimization

PSO+: A new particle swarm optimization algorithm for constrained problems

Opposition based initialization in particle swarm optimization (O-PSO)

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