Optimization using particle swarms with near neighbor interactions

Kalyan Veeramachaneni, Thanmaya Peram, Chilukuri Mohan, Lisa Ann Osadciw

Research output: Contribution to journalArticlepeer-review

133 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)110-121
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2003
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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