An Estimation of Distribution Particle Swarm Optimization Algorithm View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2006

AUTHORS

Mudassar Iqbal , Marco A. Montes de Oca

ABSTRACT

In this paper we present an estimation of distribution particle swarm optimization algorithm that borrows ideas from recent developments in ant colony optimization which can be considered an estimation of distribution algorithm. In the classical particle swarm optimization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has no means to exploit its collective memory (represented by the array of previous best positions or pbests) to guide its search. This causes a re-exploration of already known bad regions of the search space, wasting costly function evaluations. In our approach, we use the swarm’s collective memory to probabilistically guide the particles’ movement towards the estimated promising regions in the search space. Our experiments show that this approach is able to find similar or better solutions than the canonical particle swarm optimizer with fewer function evaluations. More... »

PAGES

72-83

References to SciGraph publications

  • 2004-03. Learning probability distributions in continuous evolutionary algorithms – a comparative review in NATURAL COMPUTING
  • 2002-01. A Survey of Optimization by Building and Using Probabilistic Models in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
  • 2006. Swarm Intelligence in HANDBOOK OF NATURE-INSPIRED AND INNOVATIVE COMPUTING
  • 2004. Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004. Proceedings in NONE
  • 2006. Ant Colony Optimization in METAHEURISTIC PROCEDURES FOR TRAINING NEUTRAL NETWORKS
  • 2004. ACO for Continuous and Mixed-Variable Optimization in ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE
  • Book

    TITLE

    Ant Colony Optimization and Swarm Intelligence

    ISBN

    978-3-540-38482-3
    978-3-540-38483-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/11839088_7

    DOI

    http://dx.doi.org/10.1007/11839088_7

    DIMENSIONS

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