Setup of fuzzy hybrid particle swarms: A heuristic approach

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Abstract

This paper presents a framework for systematically investigating and designing fuzzy rulesets for Adaptive Fuzzy Particle Swarm Optimization (AFPSO) algorithms. Training is achieved through Gaussian Process (GP) supported by Gradient Boosted Regression Trees (GBRT). Meta-objective was defined by ranks on various benchmark functions. Validation benchmarks were also performed on GECCO ’20 bound-constrained optimization competition. The resulting variants, particularly those controlling hybridization with Quantum Particle Swarm Optimization (QPSO) surpassed classical Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) on the training functions. Some level of generalization was also observed on most of the validation set.
Original languageEnglish
Title of host publicationGECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
Place of PublicationNew York
PublisherACM Press
Pages207-208
Number of pages2
ISBN (Electronic)9781450383516
ISBN (Print)978-1-4503-8351-6
DOIs
Publication statusPublished - 7 Jul 2021
EventGECCO '21: Genetic and Evolutionary Computation Conference - Lille, France
Duration: 10 Jul 202114 Jul 2021
Conference number: 21

Publication series

NameGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Conference

ConferenceGECCO '21: Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO
Country/TerritoryFrance
CityLille
Period10/07/2114/07/21

Keywords

  • particle swarm optimization
  • meta-heuristics
  • fuzzy control
  • ACM proceedings
  • heuristic
  • GBRT
  • PSO

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