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Glowworm Swarm Optimization for Searching Higher Dimensional Spaces

  • K. N. Krishnanand
  • D. Ghose
Part of the Studies in Computational Intelligence book series (SCI, volume 248)

Abstract

This chapter will deal with the problem of searching higher dimensional spaces using glowworm swarm optimization (GSO), a novel swarm intelligence algorithm, which was recently proposed for simultaneous capture of multiple optima of multimodal functions. Tests are performed on a set of three benchmark functions and the average peak-capture fraction is used as an index to analyze GSO’s performance as a function of dimension number. Results reported from tests conducted up to a maximum of eight dimensions show the efficacy of GSO in capturing multiple peaks in high dimensions. With an ability to search for local peaks of a function (which is the measure of fitness) in high dimensions, GSO can be applied to identification of multiple data clusters, satisfying some measure of fitness defined on the data, in high dimensional databases.

Keywords

Particle Swarm Optimization Multimodal Function Global Good Position Multiple Optimum Objective Function Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • K. N. Krishnanand
    • 1
  • D. Ghose
    • 2
  1. 1.Department of Computer ScienceUniversity of VermontBurligtonUSA
  2. 2.Guidance, Control and Decision Systems Laboratoty (GCDSL), Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia

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