research-article

Particle swarm optimization with triggered mutation and its implementation based on GPU

Authors:

Ying TanAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 1 - 8

https://doi.org/10.1145/1830483.1830485

Published: 07 July 2010 Publication History

Abstract

A novel particle swarm optimization with triggered mutation (PSO-TM) is presented in this paper for better performance. First, a technique is designed to evaluate the "health" of swarm. When the swarm is successively "unhealthy" for a certain number of iterations, uniform mutation is applied to the position of each particle in a probabilistic way. If the mutations produce worse particles, the memorized previous positions are retrieved as current positions of these particles, hence the normal evolution process of the swarm will not be fiercely interrupted by such bad mutations. Experiments are conducted on 29 benchmark test functions to show the promising performance of our proposed PSOTM. The results show that the PSO-TM performs much better than the standard PSO on almost all of the 29 test functions, especially those multimodal, complex ones of hybrid composition. Besides, PSO-TM adds little computation complexity to the standard PSO, and runs almost equally fast. Furthermore, we have implemented PSO-TM based on Graphic Processing Unit(GPU) in parallel. Compared with the CPU-based standard PSO, the proposed PSO-TM can reach a speedup of 25×, as well as an improved optimizing performance.

References

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D. Bratton, J. Kennedy, Defining a Standard for Particle Swarm Optimization, IEEE Swarm Intelligence Symposium, April 2007, pp.120--127.

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You Zhou, Ying Tan, GPU-based parallel particle swarm optimization, IEEE congress on Evolutionary Computation 18--21 May 2009. Page(s):1493 - 1500.

Digital Library

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Cited By

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https://doi.org/10.1109/ICNC-FSKD64080.2024.10702201
Li SSun Y(2019)Garden balsam optimization algorithmConcurrency and Computation: Practice and Experience10.1002/cpe.545632:2Online publication date: 24-Jul-2019
https://doi.org/10.1002/cpe.5456
Luo HXu WTan Y(2018)A Discrete Fireworks Algorithm for Solving Large-Scale Travel Salesman Problem2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477992(1-8)Online publication date: Jul-2018
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Index Terms

Particle swarm optimization with triggered mutation and its implementation based on GPU
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2010

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GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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

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Cited By

Jin CLiu G(2024)On the Impact of the Large Population on Evolutionary Algorithm2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/ICNC-FSKD64080.2024.10702201(1-10)Online publication date: 27-Jul-2024
https://doi.org/10.1109/ICNC-FSKD64080.2024.10702201
Li SSun Y(2019)Garden balsam optimization algorithmConcurrency and Computation: Practice and Experience10.1002/cpe.545632:2Online publication date: 24-Jul-2019
https://doi.org/10.1002/cpe.5456
Luo HXu WTan Y(2018)A Discrete Fireworks Algorithm for Solving Large-Scale Travel Salesman Problem2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477992(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/CEC.2018.8477992
Majd ASahebi GDaneshtalab MPlosila JLotfi STenhunen H(2018)Parallel imperialist competitive algorithmsConcurrency and Computation: Practice and Experience10.1002/cpe.439330:7Online publication date: 16-Jan-2018
https://doi.org/10.1002/cpe.4393
Chen Jin Qin A(2017)A GPU-based implementation of brain storm optimization2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969635(2698-2705)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969635
Dali NBouamama S(2017)Different parallelism levels using GPU for solving Max-CSPs with PSO2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969555(2070-2077)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969555
Kołodziejczyk JSychel DBera A(2017)Improved CUDA PSO Based on Global TopologyArtificial Intelligence and Soft Computing10.1007/978-3-319-59063-9_31(347-358)Online publication date: 27-May-2017
https://doi.org/10.1007/978-3-319-59063-9_31
Narjess DSadok BNeumann FSutton A(2016)A New Hybrid GPU-PSO Approach for Solving Max-CSPsProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2908973(119-120)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2908973
Franz WThulasiraman P(2016)A dynamic cooperative hybrid MPSO+GA on hybrid CPU+GPU fused multicore2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850283(1-8)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850283
Sojoudi Ziyabari SShahbahrami A(2015)High performance implementation of APSO algorithm using GPU platform2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP.2015.7123524(196-200)Online publication date: Mar-2015
https://doi.org/10.1109/AISP.2015.7123524
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