Article

On the moments of the sampling distribution of particle swarm optimisers

Author:

Riccardo PoliAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

Pages 2907 - 2914

https://doi.org/10.1145/1274000.1274075

Published: 07 July 2007 Publication History

Abstract

A method is presented that allows one to exactly determine all the characteristics of a PSO's sampling distribution and explain how it changes over time, in the presence stochasticity. The only assumption made is stagnation (particles are in search for a better personal best).

References

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Poli, R. and Broomhead, D. (2007), Exact Analysis of the Sampling Distribution for the Canonical Particle Swarm Optimiser and its Convergence during Stagnation, Genetic and Evolutionary Computation Conference (GECCO), London (accepted).

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Cited By

Diez MSerani ALeotardi CCampana EFasano GGusso R(2016)Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit+Advances in Swarm Intelligence10.1007/978-3-319-41000-5_32(322-330)Online publication date: 15-Jun-2016
https://doi.org/10.1007/978-3-319-41000-5_32
Cleghorn CEngelbrecht A(2015)Fully informed particle swarm optimizer: Convergence analysis2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7256888(164-170)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7256888
Cleghorn CEngelbrecht A(2015)Particle swarm variants: standardized convergence analysisSwarm Intelligence10.1007/s11721-015-0109-79:2-3(177-203)Online publication date: 2-Jun-2015
https://doi.org/10.1007/s11721-015-0109-7
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Index Terms

On the moments of the sampling distribution of particle swarm optimisers
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

July 2007

1450 pages

ISBN:9781595936981

DOI:10.1145/1274000

Program Chairs:
Peter A. N. Bosman
Centre for Mathematics & Computer Science, The Netherlands
,
Tina Yu
Memorial University of Newfoundland, Canada
,
Anikó Ekárt
Aston University, UK

Copyright © 2007 ACM.

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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Published: 07 July 2007

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, United Kingdom

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

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Cited By

Diez MSerani ALeotardi CCampana EFasano GGusso R(2016)Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit+Advances in Swarm Intelligence10.1007/978-3-319-41000-5_32(322-330)Online publication date: 15-Jun-2016
https://doi.org/10.1007/978-3-319-41000-5_32
Cleghorn CEngelbrecht A(2015)Fully informed particle swarm optimizer: Convergence analysis2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7256888(164-170)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7256888
Cleghorn CEngelbrecht A(2015)Particle swarm variants: standardized convergence analysisSwarm Intelligence10.1007/s11721-015-0109-79:2-3(177-203)Online publication date: 2-Jun-2015
https://doi.org/10.1007/s11721-015-0109-7
Diez MSerani ALeotardi CCampana EPeri DIemma UFasano GGiove S(2014)A Proposal of PSO Particles’ Initialization for Costly Unconstrained Optimization Problems: ORTHOinitAdvances in Swarm Intelligence10.1007/978-3-319-11857-4_14(126-133)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11857-4_14
Hu JWang ZQiao SGan J(2011)The fitness evaluation strategy in particle swarm optimizationApplied Mathematics and Computation10.1016/j.amc.2011.03.108217:21(8655-8670)Online publication date: Jul-2011
https://doi.org/10.1016/j.amc.2011.03.108
Clerc M(2011)From Theory to Practice in Particle Swarm OptimizationHandbook of Swarm Intelligence10.1007/978-3-642-17390-5_1(3-36)Online publication date: 2011
https://doi.org/10.1007/978-3-642-17390-5_1
de Oca MStützle TBirattari MDorigo M(2009)Frankenstein's PSOIEEE Transactions on Evolutionary Computation10.1109/TEVC.2009.202146513:5(1120-1132)Online publication date: 1-Oct-2009
https://dl.acm.org/doi/10.1109/TEVC.2009.2021465
Junqi Zhang Ying Tan Xingui He (2009)Concentric spatial extension based particle swarm optimization inspired by brood sorting in ant colonies2009 IEEE Swarm Intelligence Symposium10.1109/SIS.2009.4937838(9-15)Online publication date: Mar-2009
https://doi.org/10.1109/SIS.2009.4937838
Tan YZhang J(2009)Magnifier Particle Swarm OptimizationNature-Inspired Algorithms for Optimisation10.1007/978-3-642-00267-0_10(279-298)Online publication date: 2009
https://doi.org/10.1007/978-3-642-00267-0_10
Zhang JLiu KTan YHe XRyan C(2008)Allocation of local and global search capabilities of particle in canonical PSOProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389121(165-166)Online publication date: 13-Jul-2008
https://dl.acm.org/doi/10.1145/1389095.1389121
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