research-article

Runtime analysis of binary PSO

Authors:

Carsten WittAuthors Info & Claims

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

Pages 135 - 142

https://doi.org/10.1145/1389095.1389114

Published: 12 July 2008 Publication History

Abstract

We investigate the runtime of the Binary Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart (1997). The Binary PSO maintains a global best solution and a swarm of particles. Each particle consists of a current position, an own best position and a velocity vector used in a probabilistic process to update the particle's position. We present lower bounds for swarms of polynomial size. To prove upper bounds, we transfer a fitness-level argument well-established for evolutionary algorithms (EAs) to PSO. This method is applied to estimate the expected runtime on the class of unimodal functions. A simple variant of the Binary PSO is considered in more detail. The 1-PSO only maintains one particle, hence own best and global best solutions coincide. Despite its simplicity, the 1-PSO is surprisingly efficient. A detailed analysis for the function OneMax shows that the 1-PSO is competitive to EAs.

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Cited By

Macedo MSiqueira HFigueiredo ESantana CLira RGokhale ABastos-Filho C(2021)Overview on Binary Optimization Using Swarm-Inspired AlgorithmsIEEE Access10.1109/ACCESS.2021.31247109(149814-149858)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124710
Mühlenthaler MRaß ASchmitt MWanka R(2021)Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMaxNatural Computing10.1007/s11047-021-09856-021:4(651-677)Online publication date: 13-Jun-2021
https://doi.org/10.1007/s11047-021-09856-0
Banka HDara S(2019)A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validationPattern Recognition Letters10.1016/j.patrec.2014.10.00752:C(94-100)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.patrec.2014.10.007
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Index Terms

Runtime analysis of binary PSO
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

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

Copyright © 2008 ACM.

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Publication History

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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Cited By

Macedo MSiqueira HFigueiredo ESantana CLira RGokhale ABastos-Filho C(2021)Overview on Binary Optimization Using Swarm-Inspired AlgorithmsIEEE Access10.1109/ACCESS.2021.31247109(149814-149858)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124710
Mühlenthaler MRaß ASchmitt MWanka R(2021)Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMaxNatural Computing10.1007/s11047-021-09856-021:4(651-677)Online publication date: 13-Jun-2021
https://doi.org/10.1007/s11047-021-09856-0
Banka HDara S(2019)A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validationPattern Recognition Letters10.1016/j.patrec.2014.10.00752:C(94-100)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.patrec.2014.10.007
Doerr BTakadama K(2018)Better runtime guarantees via stochastic domination (hot-off-the-press track at GECCO 2018)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208209(13-14)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208209
Zhang XHuang KYu MJiang XYan X(2018)BFCO: A BPSO-Based Fine-Grained Communication Optimization Method for MPSoCIEEE Access10.1109/ACCESS.2018.28130026(18771-18785)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2813002
Saremi SMirjalili SLewis A(2018)How important is a transfer function in discrete heuristic algorithmsNeural Computing and Applications10.1007/s00521-014-1743-526:3(625-640)Online publication date: 27-Dec-2018
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Fournier HTeytaud O(2018)Lower Bounds for Comparison Based Evolution Strategies Using VC-dimension and Sign PatternsAlgorithmica10.1007/s00453-010-9391-359:3(387-408)Online publication date: 31-Dec-2018
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Mühlenthaler MRaß ASchmitt MSiegling AWanka RIgel CSudholt DWitt C(2017)Runtime Analysis of a Discrete Particle Swarm Optimization Algorithm on Sorting and OneMaxProceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3040718.3040721(13-24)Online publication date: 12-Jan-2017
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Dara SBanka HAnnavarapu C(2017)A Rough Based Hybrid Binary PSO Algorithm for Flat Feature Selection and Classification in Gene Expression DataAnnals of Data Science10.1007/s40745-017-0106-34:3(341-360)Online publication date: 11-Mar-2017
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