Overview of Algorithms for Swarm Intelligence

Shu-Chuan Chu, Huang Hsiang-Cheh, John Roddick, Jeng-Shyang Pan

    Research output: Contribution to conferencePaper

    27 Citations (Scopus)

    Abstract

    Swarm intelligence (SI) is based on collective behavior of self-organized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.

    Original languageEnglish
    Pages28-41
    Number of pages14
    DOIs
    Publication statusPublished - 27 Sep 2011
    Event3rd International Conference, ICCCI 2011 -
    Duration: 21 Sep 2011 → …

    Conference

    Conference3rd International Conference, ICCCI 2011
    Period21/09/11 → …

    Keywords

    • Ant Colony System (ACS)
    • Artificial Bee Colony (ABC)
    • Particle Swarm Optimization (PSO)
    • Swarm intelligence (SI)

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  • Cite this

    Chu, S-C., Hsiang-Cheh, H., Roddick, J., & Pan, J-S. (2011). Overview of Algorithms for Swarm Intelligence. 28-41. Paper presented at 3rd International Conference, ICCCI 2011, . https://doi.org/10.1007/978-3-642-23935-9_3