Article

Understanding particle swarms through simplification: a study of recombinant PSO

Authors:

Daniel Bratton,

Tim BlackwellAuthors Info & Claims

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

Pages 2621 - 2628

https://doi.org/10.1145/1274000.1274078

Published: 07 July 2007 Publication History

Get Access

Abstract

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding of what makes a PSO swarm function in the way that it does. One of these forms, PSO with discrete recombination, is analyzed in depth, demonstrating not just improvements in performance to a standard PSO algorithm, but also significantly different behavior with a reduction in bursting patterns due to the removal of stochastic components from the update equations. This altered behavior accompanied by equal and improved performance leads to conjectures that bursts are not generally efficacious in the optimization process.

References

[1]

T. M. Blackwell and D. Bratton. Origin of bursts. In Submitted to Particles swarms: the second decade, workshop, GECCO 2007, 2007.

Digital Library

Google Scholar

[2]

D. Bratton and J. Kennedy. Defining a standard for particle swarm optimization. In IEEE swarm intelligence symposium, 2007.

Google Scholar

[3]

D. K. Gehlhaar and D. B. Fogel. Tuning evolutionary programming. 1996.

Google Scholar

[4]

J. Jaccard and C. Wan. LISREL approaches to interaction effects in multiple regression. Sage Publications, Thousand Oaks, CA, 1996.

Crossref

Google Scholar

[5]

J. Kennedy. Probability and dynamics in the particle swarm. In IEEE congress on evolutionary computation, pages 340--347, 2004.

Google Scholar

[6]

C. Monson and K. Seppi. Exposing origin-seeking bias in pso. In Proceedings of the 2005 Genetic and Evolutionary Computation Conference, pages 241--248, 2005.

Digital Library

Google Scholar

[7]

J. Pena, A. Upegui, and E. Sanchez. Particle swarm optimization with discrete recombination: an online optimizer for evolvable hardware. In First NASA/ESA conference on adaptive hardware and systems, AHS '06, pages 163--170, 2007.

Digital Library

Google Scholar

[8]

T. Richer and T. Blackwell. The levy particle swarm. In IEEE congress on evolutionary computation, pages 3150--3157, 2006.

Google Scholar

Cited By

View all

Macedo MTaw LGurrapadi NLira RPinheiro DOliveira MBastos-Filho CMenezes RKrawiec K(2021)Fishing for interactionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459327(40-48)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459327
Jin XDuan YZhang YHuang YLi MMao MSingh ALi Y(2021)Fast Search of Lightweight Block Cipher Primitives via Swarm-like Metaheuristics for Cyber SecurityACM Transactions on Internet Technology10.1145/341729621:4(1-15)Online publication date: 16-Jul-2021
https://dl.acm.org/doi/10.1145/3417296
Taw LGurrapadi NMacedo MOliveira MPinheiro DBastos-Filho CMenezes R(2019)Characterizing the Social Interactions in the Artificial Bee Colony Algorithm2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8789898(1243-1250)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8789898
Show More Cited By

Index Terms

Understanding particle swarms through simplification: a study of recombinant PSO
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

Recommendations

A Heterogeneous Particle Swarm
ACAL '09: Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology

Almost all Particle Swarm Optimisation (PSO) algorithms use a number of identical, interchangeable particles that show the same behaviour throughout an optimisation. This paper describes a PSO algorithm in which the particles, while still identical, ...
Exploring extended particle swarms: a genetic programming approach
GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Particle Swarm Optimisation (PSO) uses a population of particles that fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both towards the best point sampled by ...
Particle Swarms for Linearly Constrained Optimisation

Particle Swarm Optimisation (PSO) has proved to be a very useful algorithm to optimise unconstrained functions. This paper extends PSO to a Linear PSO (LPSO) to optimise functions constrained by a set of equality constraints of the form Ax=b. By ...

Comments

Information & Contributors

Information

Published In

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

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

GECCO07

Sponsor:

GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, United Kingdom

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
360
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Macedo MTaw LGurrapadi NLira RPinheiro DOliveira MBastos-Filho CMenezes RKrawiec K(2021)Fishing for interactionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459327(40-48)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459327
Jin XDuan YZhang YHuang YLi MMao MSingh ALi Y(2021)Fast Search of Lightweight Block Cipher Primitives via Swarm-like Metaheuristics for Cyber SecurityACM Transactions on Internet Technology10.1145/341729621:4(1-15)Online publication date: 16-Jul-2021
https://dl.acm.org/doi/10.1145/3417296
Taw LGurrapadi NMacedo MOliveira MPinheiro DBastos-Filho CMenezes R(2019)Characterizing the Social Interactions in the Artificial Bee Colony Algorithm2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8789898(1243-1250)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8789898
Gurrapadi NTaw LMacedo MOliveira MPinheiro DBastos-Filho CMenezes R(2019)Modelling the Social Interactions in Ant Colony OptimizationIntelligent Data Engineering and Automated Learning – IDEAL 201910.1007/978-3-030-33617-2_23(216-224)Online publication date: 18-Oct-2019
https://doi.org/10.1007/978-3-030-33617-2_23
Smith CKanvinde ADeierlein G(2017)Calibration of Continuum Cyclic Constitutive Models for Structural Steel Using Particle Swarm OptimizationJournal of Engineering Mechanics10.1061/(ASCE)EM.1943-7889.0001214143:5Online publication date: May-2017
https://doi.org/10.1061/(ASCE)EM.1943-7889.0001214
Li LHe X(2009)Gaussion Mutation Particle Swarm Optimization with Dynamic Adaptation Inertia WeightProceedings of the 2009 WRI World Congress on Software Engineering - Volume 0410.1109/WCSE.2009.24(454-459)Online publication date: 19-May-2009
https://dl.acm.org/doi/10.1109/WCSE.2009.24
Ghosh SKundu DSuresh KDas SAbraham APanigrahi BSnasel V(2009)On Some Properties of the lbest Topology in Particle Swarm OptimizationProceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 0310.1109/HIS.2009.288(370-375)Online publication date: 12-Aug-2009
https://dl.acm.org/doi/10.1109/HIS.2009.288
Blackwell TBratton D(2008)Examination of particle tailsJournal of Artificial Evolution and Applications10.1155/2008/8932372008:S1(1-10)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1155/2008/893237
Peña JRyan C(2008)Theoretical and empirical study of particle swarms with additive stochasticity and different recombination operatorsProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389109(95-102)Online publication date: 13-Jul-2008
https://dl.acm.org/doi/10.1145/1389095.1389109
Blackwell TBratton DBosman PYu TEkárt A(2007)Origin of burstsProceedings of the 9th annual conference companion on Genetic and evolutionary computation10.1145/1274000.1274076(2613-2620)Online publication date: 7-Jul-2007
https://dl.acm.org/doi/10.1145/1274000.1274076

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A Heterogeneous Particle Swarm

Exploring extended particle swarms: a genetic programming approach

Particle Swarms for Linearly Constrained Optimisation

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations