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Convergence behavior of the fully informed particle swarm optimization algorithm

Authors:

Marco A. Montes de Oca,

Thomas StützleAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 71 - 78

https://doi.org/10.1145/1389095.1389106

Published: 12 July 2008 Publication History

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Abstract

The fully informed particle swarm optimization algorithm (FIPS) is very sensitive to changes in the population topology. The velocity update rule used in FIPS considers all the neighbors of a particle to update its velocity instead of just the best one as it is done in most variants. It has been argued that this rule induces a random behavior of the particle swarm when a fully connected topology is used. This argument could explain the often observed poor performance of the algorithm under that circumstance.

In this paper we study experimentally the convergence behavior of the particles in FIPS when using topologies with different levels of connectivity. We show that the particles tend to search a region whose size decreases as the connectivity of the population topology increases. We therefore put forward the idea that spatial convergence, and not a random behavior, is the cause of the poor performance of FIPS with a fully connected topology. The practical implications of this result are explored.

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Khan AShafi IKhawaja Sde la Torre Díez IFlores MGalvlán JAshraf I(2023)Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization VariantsSensors10.3390/s2318771023:18(7710)Online publication date: 6-Sep-2023
https://doi.org/10.3390/s23187710
Vafashoar RMorshedlou HMeybodi M(2021)Bifurcated particle swarm optimizer with topology learning particlesApplied Soft Computing10.1016/j.asoc.2021.108039114:COnline publication date: 30-Dec-2021
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Convergence behavior of the fully informed particle swarm optimization algorithm

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Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Khan AShafi IKhawaja Sde la Torre Díez IFlores MGalvlán JAshraf I(2023)Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization VariantsSensors10.3390/s2318771023:18(7710)Online publication date: 6-Sep-2023
https://doi.org/10.3390/s23187710
Vafashoar RMorshedlou HMeybodi M(2021)Bifurcated particle swarm optimizer with topology learning particlesApplied Soft Computing10.1016/j.asoc.2021.108039114:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.108039
Hasan MAl-Rizzo H(2019)Optimization of Sensor Deployment for Industrial Internet of Things Using a Multiswarm AlgorithmIEEE Internet of Things Journal10.1109/JIOT.2019.29384866:6(10344-10362)Online publication date: Dec-2019
https://doi.org/10.1109/JIOT.2019.2938486
Hasan MAl-Turjman F(2017)Optimizing Multipath Routing With Guaranteed Fault Tolerance in Internet of ThingsIEEE Sensors Journal10.1109/JSEN.2017.273918817:19(6463-6473)Online publication date: 1-Oct-2017
https://doi.org/10.1109/JSEN.2017.2739188
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
Ni QCao CYin X(2014)A new dynamic probabilistic Particle Swarm Optimization with dynamic random population topology2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900381(1321-1327)Online publication date: Jul-2014
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Bonyadi MMichalewicz Z(2014)A locally convergent rotationally invariant particle swarm optimization algorithmSwarm Intelligence10.1007/s11721-014-0095-18:3(159-198)Online publication date: 1-Aug-2014
https://doi.org/10.1007/s11721-014-0095-1
Nebro ADurillo JCoello C(2013)Analysis of leader selection strategies in a multi-objective Particle Swarm Optimizer2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557955(3153-3160)Online publication date: Jun-2013
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Garcia-Nieto JAlba EMoore J(2012)Why six informants is optimal in PSOProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330168(25-32)Online publication date: 7-Jul-2012
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Particle Swarm Optimization inspired by starling flock behavior

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