Setup of fuzzy hybrid particle swarms: A heuristic approach

Nicolas Roy; Charlotte BEAUTHIER; Timoteo Carletti; Alexandre Mayer

doi:10.1145/3449726.3459418

Setup of fuzzy hybrid particle swarms: A heuristic approach

Nicolas Roy, Charlotte BEAUTHIER, Timoteo Carletti, Alexandre Mayer

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Résumé

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.

langue originale	Anglais
titre	Proceedings of the Genetic and Evolutionary Computation Conference Companion
Lieu de publication	New York
Editeur	ACM Press
Pages	207-208
Nombre de pages	2
ISBN (Electronique)	9781450383516
ISBN (imprimé)	978-1-4503-8351-6
Les DOIs	https://doi.org/10.1145/3449726.3459418
Etat de la publication	Publié - 7 juil. 2021
Evénement	GECCO '21: Genetic and Evolutionary Computation Conference - Lille, France Durée: 10 juil. 2021 → 14 juil. 2021 Numéro de conférence: 21

Série de publications

Nom	GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Une conférence

Une conférence	GECCO '21: Genetic and Evolutionary Computation Conference
Titre abrégé	GECCO
Pays/Territoire	France
La ville	Lille
période	10/07/21 → 14/07/21

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Setup Of Fuzzy Hybrid Particle Swarms - A Heuristic ApproachVersion finale publiée, 585 KBLicense: Non spécifié

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1/01/18 → 31/12/22
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Roy, N., BEAUTHIER, C., Carletti, T., & Mayer, A. (2021). Setup of fuzzy hybrid particle swarms: A heuristic approach. Dans Proceedings of the Genetic and Evolutionary Computation Conference Companion (p. 207-208). (GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion). ACM Press. https://doi.org/10.1145/3449726.3459418

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title = "Setup of fuzzy hybrid particle swarms: A heuristic approach",

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 {\textquoteright}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.",

keywords = "particle swarm optimization, meta-heuristics, fuzzy control, ACM proceedings, heuristic, GBRT, PSO",

author = "Nicolas Roy and Charlotte BEAUTHIER and Timoteo Carletti and Alexandre Mayer",

note = "Funding Information: 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 {\textquoteright}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. Publisher Copyright: {\textcopyright} 2021 Owner/Author.; GECCO '21: Genetic and Evolutionary Computation Conference, GECCO ; Conference date: 10-07-2021 Through 14-07-2021",

year = "2021",

month = jul,

day = "7",

doi = "10.1145/3449726.3459418",

language = "English",

isbn = "978-1-4503-8351-6",

series = "GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",

publisher = "ACM Press",

pages = "207--208",

booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",

address = "United States",

}

Roy, N, BEAUTHIER, C, Carletti, T & Mayer, A 2021, Setup of fuzzy hybrid particle swarms: A heuristic approach. Dans Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, ACM Press, New York, p. 207-208, GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France, 10/07/21. https://doi.org/10.1145/3449726.3459418

Setup of fuzzy hybrid particle swarms: A heuristic approach. / Roy, Nicolas; BEAUTHIER, Charlotte; Carletti, Timoteo et al.
Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM Press, 2021. p. 207-208 (GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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AB - 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.

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Setup of fuzzy hybrid particle swarms: A heuristic approach

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Projets

CÉCI – Consortium des Équipements de Calcul Intensif

Équipement

Plateforme Technologique Calcul Intensif

Contient cette citation