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Article

Preventing premature convergence in a PSO and EDA hybrid

Author:

Mohammed El-AbdAuthors Info & Claims

CEC'09: Proceedings of the Eleventh conference on Congress on Evolutionary Computation

Pages 3060 - 3066

Published: 18 May 2009 Publication History

Abstract

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from earlier attempts to simulate the behavior of birds and was successfully applied in many applications as an optimization tool. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms which build a probabilistic model capturing the search space properties and use this model to generate new individuals. One research trend that emerged in the past few years is the hybridization of PSO and EDA algorithms. In this work, we examine one of these hybrids attempts that uses a Gaussian model for capturing the search space characteristics. We compare two different approaches, previously introduced into EDAs to prevent premature convergence, when incorporated into this hybrid algorithm. The performance of the hybrid algorithm with and without these approaches is studied using a suite of well-known benchmark optimization functions.

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Cited By

Cheng RYao M(2010)A modified particle swarm optimizer with a novel operatorProceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II10.5555/1926560.1926606(293-301)Online publication date: 23-Oct-2010
https://dl.acm.org/doi/10.5555/1926560.1926606
El-Abd MKamel MRothlauf F(2009)Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybridProceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers10.1145/1570256.1570315(2263-2268)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1570256.1570315

Preventing premature convergence in a PSO and EDA hybrid
1. Information systems
  1. Data management systems
    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization

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cover image Guide Proceedings

CEC'09: Proceedings of the Eleventh conference on Congress on Evolutionary Computation

May 2009

3356 pages

ISBN:9781424429585

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IEEE Press

Publication History

Published: 18 May 2009

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Cited By

Cheng RYao M(2010)A modified particle swarm optimizer with a novel operatorProceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II10.5555/1926560.1926606(293-301)Online publication date: 23-Oct-2010
https://dl.acm.org/doi/10.5555/1926560.1926606
El-Abd MKamel MRothlauf F(2009)Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybridProceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers10.1145/1570256.1570315(2263-2268)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1570256.1570315

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