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PSO with randomized low-discrepancy sequences

Published: 07 July 2007 Publication History

Abstract

We initialize a global-best particle swarm with a Halton sequence, comparing it with uniform initialization on a range of benchmark function optimization problems. We see substantial improvements in performance, particularly with high complexity problems /small populations. Halton initialization yields equivalent performance to uniform initialization with substantially smaller populations.

References

[1]
Daida, J, Ampy, D, Ratanasavetavadhana, M, Li, H, & Chaudhri, O Challenges with Verification, Repeatability, and Meaningful Comparison in Genetic Programming: Gibson's Magic. In Proceedings of GECCO 1999, 1851--1858, 1999
[2]
Kimura, S & Matsumura, K Genetic Algorithms using Low-Discrepancy Sequences In Proc GECCO 2005, 1341--1346, 2005.
[3]
Meysenburg, M.M. & Foster, J.A. Random Generator Quality and GP Performance. In Proceedings GECCO 1999, 1121--1126, 1999.
[4]
Gentle, E.J. Random Number Generation and Monte Carlo Methods, Springer-Verlag, 1998.
[5]
Morokoff,W.J. & Caflisch,R.E. . Quasi-random sequences and their discrepancies. SIAM J Sci Comp, 15(6): 1251--1279, 1994.
[6]
Wang, X. & Hickernell, F.J. Randomized Halton Sequences, Mathematical and Computer Modelling, 32: 887--899, 2000.
[7]
Engelbrecht, A.P. Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, 2005.
[8]
Press, W.H., Teukolsky, S.A., Vetterling, W.T., & Flannery, B.P. Numerical Recipes in C++: The Art of Scientific Computing, Cambridge University Press, chapter 7, 2002.

Cited By

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  • (2024)Innovative Initialization Scheme for Multi-Objective Feature Selection in Continuous Search Spaces2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831698(128-135)Online publication date: 6-Oct-2024
  • (2024)Exploring the Impact of Random Distribution Choices on Particle Swarm OptimizationProcedia Computer Science10.1016/j.procs.2023.10.493225:C(4930-4942)Online publication date: 4-Mar-2024
  • (2021)Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization ProblemsApplied Sciences10.3390/app1116759111:16(7591)Online publication date: 18-Aug-2021
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Published In

cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. particle swarm optimization
  2. randomized Halton sequence

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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View all
  • (2024)Innovative Initialization Scheme for Multi-Objective Feature Selection in Continuous Search Spaces2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831698(128-135)Online publication date: 6-Oct-2024
  • (2024)Exploring the Impact of Random Distribution Choices on Particle Swarm OptimizationProcedia Computer Science10.1016/j.procs.2023.10.493225:C(4930-4942)Online publication date: 4-Mar-2024
  • (2021)Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization ProblemsApplied Sciences10.3390/app1116759111:16(7591)Online publication date: 18-Aug-2021
  • (2016)Particle SwarmsMetaheuristics10.1007/978-3-319-45403-0_8(203-228)Online publication date: 25-Dec-2016
  • (2010)Dynamic analysis for the selection of parameters and initial population, in particle swarm optimizationJournal of Global Optimization10.1007/s10898-009-9493-048:3(347-397)Online publication date: 1-Nov-2010
  • (2008)Opposition-Based ComputingOppositional Concepts in Computational Intelligence10.1007/978-3-540-70829-2_2(11-28)Online publication date: 2008

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