research-article

Cooperative micro-particle swarm optimization

Author:

Konstantinos E. ParsopoulosAuthors Info & Claims

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

Pages 467 - 474

https://doi.org/10.1145/1543834.1543897

Published: 12 June 2009 Publication History

Abstract

Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low-dimensional subpopulations. On the other hand, Micro-evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative Micro-Particle Swarm Optimization, which employs cooperative low-dimensional and low-cardinality subswarms to concurrently adapt different subcomponents of high-dimensional optimization problems. The algorithm is applied on high-dimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle Swarm Optimization algorithm are also reported and discussed.

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Cited By

Panoc TMeca OTomaszek LBrzobohatý TŘíha LZelinka IKozubek T(2024)Automated search of an optimal configuration of FETI-based algorithms with the swarm and evolutionary algorithmsApplied Soft Computing10.1016/j.asoc.2024.112437167:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112437
Salehinejad HRahnamayan STizhoosh H(2017)Micro-differential evolutionApplied Soft Computing10.1016/j.asoc.2016.09.04252:C(812-833)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.asoc.2016.09.042
Caraffini FNeri FIacca G(2017)Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among VariablesApplications of Evolutionary Computation10.1007/978-3-319-55849-3_41(636-652)Online publication date: 25-Mar-2017
https://doi.org/10.1007/978-3-319-55849-3_41
Show More Cited By

Index Terms

Cooperative micro-particle swarm optimization
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
  2. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

cover image ACM Conferences

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

June 2009

1112 pages

ISBN:9781605583266

DOI:10.1145/1543834

General Chairs:
Lihong Xu
Tongji University, China
,
Erik D. Goodman
Michigan State University, USA
,
Program Chairs:
Guoliang Chen
University of Sci. & Tech. of China
,
Darrell Whitley
Colorado State University, USA
,
Yongsheng Ding
Donghua University, China

Copyright © 2009 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: 12 June 2009

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GEC '09

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GEC '09: GEC '09 - ACM/SIGEVO Genetic and Evolutionary Computation Summit

June 12 - 14, 2009

Shanghai, China

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Cited By

Panoc TMeca OTomaszek LBrzobohatý TŘíha LZelinka IKozubek T(2024)Automated search of an optimal configuration of FETI-based algorithms with the swarm and evolutionary algorithmsApplied Soft Computing10.1016/j.asoc.2024.112437167:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112437
Salehinejad HRahnamayan STizhoosh H(2017)Micro-differential evolutionApplied Soft Computing10.1016/j.asoc.2016.09.04252:C(812-833)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.asoc.2016.09.042
Caraffini FNeri FIacca G(2017)Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among VariablesApplications of Evolutionary Computation10.1007/978-3-319-55849-3_41(636-652)Online publication date: 25-Mar-2017
https://doi.org/10.1007/978-3-319-55849-3_41
Trunfio G(2015)Adaptation in Cooperative Coevolutionary OptimizationAdaptation and Hybridization in Computational Intelligence10.1007/978-3-319-14400-9_4(91-109)Online publication date: 2015
https://doi.org/10.1007/978-3-319-14400-9_4
Viveros-Jiménez FLeón-Borges JCruz-Cortés N(2012)A simple adaptive algorithm for numerical optimizationProceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II10.1007/978-3-642-37798-3_11(115-126)Online publication date: 27-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-37798-3_11
Seren C(2011)A hybrid Jumping Particle Swarm Optimization method for high dimensional unconstrained discrete problems2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949813(1649-1656)Online publication date: Jun-2011
https://doi.org/10.1109/CEC.2011.5949813

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