Archive-based swarms

Nishant Rodrigues, Chilukuri Mohan

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

Abstract

The Particle Swarm Optimization (PSO) algorithm updates each individual's velocity and position using its own prior best position and the best position found so far by any particle. Effective search for global optima can benefit if each particle may utilize additional historical information regarding the quality of previously visited positions in its proximity. We present an approach for doing so, using an archive containing some prior particle positions, analogous to the archive used in some multi-objective optimization algorithms. A specific instance of this approach is the proposed Fitness-Distance-Ratio Archive-Based Swarm Optimization algorithm, shown to outperform PSO and three other variants, when applied to high-dimensional neural network training problems.

Original languageEnglish (US)
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1460-1467
Number of pages8
ISBN (Electronic)9781450371278
DOIs
StatePublished - Jul 8 2020
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: Jul 8 2020Jul 12 2020

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period7/8/207/12/20

Keywords

  • Archive
  • Artificial neural networks
  • Evolutionary machine learning
  • Fitness-distance ratio
  • Particle swarm optimization
  • Premature convergence
  • Swarm intelligence

ASJC Scopus subject areas

  • Computational Mathematics

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  • Cite this

    Rodrigues, N., & Mohan, C. (2020). Archive-based swarms. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 1460-1467). (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3377929.3398112