Article

Particle swarm guided evolution strategy

Authors:

Chang-Tai Hsieh,

Chih-Ming Chen,

Ying-ping ChenAuthors Info & Claims

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

Pages 650 - 657

https://doi.org/10.1145/1276958.1277096

Published: 07 July 2007 Publication History

Abstract

Evolution strategy (ES) and particle swarm optimization (PSO) are two of the most popular research topics for tackling real-parameter optimization problems in evolutionary computation. Both of them have strengths and weaknesses for their different search behaviors and methodologies. In ES, mutation, as the main operator, tries to find good solutions around each individual. While in PSO, particles are moving toward directions determined by certain global information, such as the global best particle. In order to leverage the specialties offered by both sides to our advantage, this paper combines the essential mechanism of ES and the key concept of PSO to develop a new hybrid optimization methodology, called particle swarm guided evolution strategy. We introduce swarm intelligence to the ES mutation framework to create a new mutation operator, called guided mutation, and integrate the guided mutation operator into ES. Numerical experiments are conducted on a set of benchmark functions, and the experimental results indicate that PSGES is a promising optimization methodology as well as an interesting research direction.

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Cited By

Xu PLuo WLin XQiao YZhu T(2019)Hybrid of PSO and CMA-ES for Global Optimization2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8789912(27-33)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8789912
Kafafy ABonnevay SBounekkar AAlba E(2013)A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimizationProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463458(631-638)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463458
Matsumura YSugiyama KYasuda TOhkura K(2013)(μ, λ) evolutionary and particle swarm hybrid algorithm over cloud computing, with an application to dinosaur gait optimizationProceedings of the 2013 IEEE/SICE International Symposium on System Integration10.1109/SII.2013.6776759(802-807)Online publication date: Dec-2013
https://doi.org/10.1109/SII.2013.6776759
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Particle swarm guided evolution strategy

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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

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 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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Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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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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Cited By

Xu PLuo WLin XQiao YZhu T(2019)Hybrid of PSO and CMA-ES for Global Optimization2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8789912(27-33)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8789912
Kafafy ABonnevay SBounekkar AAlba E(2013)A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimizationProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463458(631-638)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463458
Matsumura YSugiyama KYasuda TOhkura K(2013)(μ, λ) evolutionary and particle swarm hybrid algorithm over cloud computing, with an application to dinosaur gait optimizationProceedings of the 2013 IEEE/SICE International Symposium on System Integration10.1109/SII.2013.6776759(802-807)Online publication date: Dec-2013
https://doi.org/10.1109/SII.2013.6776759
Matsumura YKobayashi ASugiyama KPataky TYasuda TOhkura KSellers B(2013)A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)10.1109/IWCIA.2013.6624791(89-93)Online publication date: Jul-2013
https://doi.org/10.1109/IWCIA.2013.6624791
Masrom SAbidin SOmar N(2012)A taxonomy of low-level hybridization in metaheuristics algorithms2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)10.1109/ICACI.2012.6463202(441-446)Online publication date: Oct-2012
https://doi.org/10.1109/ICACI.2012.6463202
Chen YChen C(2010)Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretizationEvolutionary Computation10.1162/evco.2010.18.2.1820218:2(199-228)Online publication date: 1-Jun-2010
https://dl.acm.org/doi/10.1162/evco.2010.18.2.18202
Müller CBaumgartner BSbalzarini I(2009)Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapesProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689956(2685-2692)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689956
Chen CChen YZhang Q(2009)Enhancing MOEA/D with guided mutation and priority update for multi-objective optimizationProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689627(209-216)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689627
Muller CBaumgartner BSbalzarini I(2009)Particle Swarm CMA Evolution Strategy for the optimization of multi-funnel landscapes2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983279(2685-2692)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983279
Chih-Ming Chen Ying-ping Chen Qingfu Zhang (2009)Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4982950(209-216)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4982950

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