Particle swarm and ant colony approaches in multiobjective optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.

Original languageEnglish (US)
Title of host publicationInternational Conference on Modeling, Optimization, and Computing, ICMOC 2010
Pages7-11
Number of pages5
DOIs
StatePublished - Dec 1 2010
EventInternational Conference on Modeling, Optimization, and Computing, ICMOC 2010 - Durgapur, West Bengal, India
Duration: Oct 28 2010Oct 30 2010

Publication series

NameAIP Conference Proceedings
Volume1298
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

OtherInternational Conference on Modeling, Optimization, and Computing, ICMOC 2010
CountryIndia
CityDurgapur, West Bengal
Period10/28/1010/30/10

Keywords

  • Ant colony optimization
  • Constrained optimization
  • Multiobjective optimizatio Game theory
  • Partcle swarm optimization
  • Unconstrained optimization

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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  • Cite this

    Rao, S. S. (2010). Particle swarm and ant colony approaches in multiobjective optimization. In International Conference on Modeling, Optimization, and Computing, ICMOC 2010 (pp. 7-11). (AIP Conference Proceedings; Vol. 1298). https://doi.org/10.1063/1.3516427