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Optimization Using Particle Swarms with Near Neighbor Interactions

  • Kalyan Veeramachaneni
  • Thanmaya Peram
  • Chilukuri Mohan
  • Lisa Ann Osadciw
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. This is accomplished by using the ratio of the relative fitness and the distance of other particles to determine the direction in which each component of the particle position needs to be changed. The resulting algorithm, known as Fitness-Distance-Ratio based PSO (FDR-PSO), is shown to perform significantly better than the original PSO algorithm and several of its variants, on many different benchmark optimization problems. Avoiding premature convergence allows FDR-PSO to continue search for global optima in difficult multimodal optimization problems, reaching better solutions than PSO and several of its variants.

Keywords

Particle Swarm Optimization Particle Swarm Particle Swarm Optimization Algorithm Benchmark Problem Premature Convergence 
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 2003

Authors and Affiliations

  • Kalyan Veeramachaneni
    • 1
  • Thanmaya Peram
    • 1
  • Chilukuri Mohan
    • 1
  • Lisa Ann Osadciw
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuse

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