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An enhanced particle swarm optimization algorithm for multi-modal functions

  • Ngai Kwok
  • , G. Fang
  • , Q. R. Ha
  • , D. K. Liu
  • University of Technology Sydney

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

4 Citations (Scopus)

Abstract

The particle swarm optimization algorithm has been frequently employed to solve various optimization problems. Although the algorithm is performing satisfactorily while tackling unit-modal optimizations, enhancements in dealing with multi-modal functions are indeed desirable. Convergence of particles to the optimum solution is a primary and traditional requirement, however, this is achieved only after all the solutions space has been covered and evaluated. In this work, the focus is directed towards maintaining sufficient divergence of particles in multi-modal problems, by developing an alternative social interaction scheme among the swarm members. Particularly, a multiple-leaders strategy is employed in the new PSO algorithm to prevent pre-mature convergence. Results from benchmark problems are included to illustrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007
Pages457-462
Number of pages6
DOIs
Publication statusPublished - 2007
Event2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 - Harbin, China
Duration: 5 Aug 20078 Aug 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007

Conference

Conference2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007
Country/TerritoryChina
CityHarbin
Period5/08/078/08/07

Keywords

  • Multi-modal functions
  • Pareto front
  • Particle swarm optimization

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