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Incremental Social Learning in Swarm Intelligence Algorithms for Continuous Optimization

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Computational Intelligence (IJCCI 2011)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 465))

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Abstract

Swarm intelligence is the collective problem-solving behavior of groups of animals and artificial agents. Often, swarm intelligence is the result of self-organization, which emerges from the agents’ local interactions with one another and with their environment. Such local interactions can be positive, negative, or neutral. Positive interactions help a swarm of agents solve a problem. Negative interactions are those that block or hinder the agents’ task-performing behavior. Neutral interactions do not affect the swarm’s performance. Reducing the effects of negative interactions is one of the main tasks of a designer of effective swarm intelligence systems. Traditionally, this has been done through the complexification of the behavior and/or the characteristics of the agents that comprise the system, which limits scalability and increases the difficulty of the design task. In collaboration with colleagues, I have proposed a framework, called incremental social learning (ISL), as a means to reduce the effects of negative interactions without complexifying the agents’ behavior or characteristics. In this paper, I describe the ISL framework and three instantiations of it, which demonstrate the framework’s effectiveness. The swarm intelligence systems used as case studies are the particle swarm optimization algorithm, ant colony optimization algorithm for continuous domains, and the artificial bee colony optimization algorithm.

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Delaware, Newark, DE, U.S.A.

    Marco A. Montes de Oca

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  1. Marco A. Montes de Oca
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Correspondence to Marco A. Montes de Oca .

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Editors and Affiliations

  1. , Images, Signals and, University PARIS-EST Creteil (UPEC), Paris 12, 3956, France

    Kurosh Madani

  2. , Departamento de Engenharia Informatica, University of Coimbra, Polo II - Pinhal de Marrocos, Coimbra, 3030, Portugal

    António Dourado

  3. Systems and Robotics Institute, Evolutionary Systems and, Instituto Superior Tecnico IST, Av. Rovisco Pais, Lisboa, 1049-001, Portugal

    Agostinho Rosa

  4. INSTICC, Polytechnic Institute of Setúbal /, Setubal, 2910-595, Portugal

    Joaquim Filipe

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de Oca, M.A.M. (2013). Incremental Social Learning in Swarm Intelligence Algorithms for Continuous Optimization. In: Madani, K., Dourado, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2011. Studies in Computational Intelligence, vol 465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35638-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-35638-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35637-7

  • Online ISBN: 978-3-642-35638-4

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