technical-note

Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2269 - 2274

https://doi.org/10.1145/1570256.1570316

Published: 08 July 2009 Publication History

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Abstract

This paper benchmarks the Particle Swarm Optimization (PSO) algorithm using the noise-free BBOB 2009 testbed.

References

[1]

R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In Proc. of the 6th International Symposium on Micro Machine and Human Science, pages 39--43, 1995.

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S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE, 2009.

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N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.

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N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions. Technical Report RR-6829, INRIA, 2009.

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J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proc. of IEEE International Conference on Neural Networks, volume 4, pages 1942--1948, 1995.

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Felix-Saul JGarcía-Valdez MMerelo Guervós JCastillo O(2024)Extending Genetic Algorithms with Biological Life-Cycle DynamicsBiomimetics10.3390/biomimetics90804769:8(476)Online publication date: 6-Aug-2024
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Shigenaka STakami STanigaki YWatanabe SOnishi M(2024)MAS-Bench: a benchmarking for parameter calibration of multi-agent crowd simulationJournal of Computational Social Science10.1007/s42001-024-00302-67:2(2121-2145)Online publication date: 18-Jul-2024
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Zaborski MWoźniak MMańdziuk J(2022)Multidimensional Red Fox meta-heuristic for complex optimization▪Applied Soft Computing10.1016/j.asoc.2022.109774131:COnline publication date: 1-Dec-2022
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Index Terms

Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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Technical-note

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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

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Felix-Saul JGarcía-Valdez MMerelo Guervós JCastillo O(2024)Extending Genetic Algorithms with Biological Life-Cycle DynamicsBiomimetics10.3390/biomimetics90804769:8(476)Online publication date: 6-Aug-2024
https://doi.org/10.3390/biomimetics9080476
Shigenaka STakami STanigaki YWatanabe SOnishi M(2024)MAS-Bench: a benchmarking for parameter calibration of multi-agent crowd simulationJournal of Computational Social Science10.1007/s42001-024-00302-67:2(2121-2145)Online publication date: 18-Jul-2024
https://doi.org/10.1007/s42001-024-00302-6
Zaborski MWoźniak MMańdziuk J(2022)Multidimensional Red Fox meta-heuristic for complex optimization▪Applied Soft Computing10.1016/j.asoc.2022.109774131:COnline publication date: 1-Dec-2022
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Okulewicz MZaborski MMańdziuk J(2022)Self-Adapting Particle Swarm Optimization for continuous black box optimizationApplied Soft Computing10.1016/j.asoc.2022.109722131(109722)Online publication date: Dec-2022
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Sun FWang LYang B(2021)A neuro-diversified benchmark generator for black box optimizationInformation Sciences10.1016/j.ins.2021.04.075573(475-492)Online publication date: Sep-2021
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Liu CSun FNi QWang LYang B(2020)A Novel Graphic Bending Transformation on Benchmark2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283291(1538-1543)Online publication date: 11-Oct-2020
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García Valdez MMerelo Guervós J(2020)A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithmsFuture Generation Computer Systems10.1016/j.future.2020.10.039Online publication date: Nov-2020
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Talbi HDraa A(2019)A Continuous Optimization Scheme Based on an Enhanced Differential Evolution and a Trust Region MethodProceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.110.1007/978-3-030-21005-2_22(222-233)Online publication date: 11-Jul-2019
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Singh SKumar A(2017)Homeostasis mutation based differential evolution algorithmJournal of Intelligent & Fuzzy Systems10.3233/JIFS-16928932:5(3525-3537)Online publication date: 24-Apr-2017
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