research-article

GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution

Authors:

Péricles Barbosa Miranda,

Ricardo Bastos PrudêncioAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1087 - 1094

https://doi.org/10.1145/2739480.2754819

Published: 11 July 2015 Publication History

Abstract

In this work, we propose a framework to automatically generate effective PSO designs by adopting Grammatical Evolution (GE). In the proposed framework, GE searches for adequate structures and parameter values (e.g., acceleration constants, velocity equations and different particles' topology) in order to evolve the PSO design. For this, a high-level Backus--Naur Form (BNF) grammar was developed, representing the search space of possible PSO designs. In order to verify the performance of the proposed method, we performed experiments using 16 diverse continuous optimization problems, with different levels of difficulty. In the performed experiments, we identified the parameters and components that most affected the PSO performance, as well as identified designs that could be reused across different problems. We also demonstrated that the proposed method generates useful designs which achieved competitive solutions when compared to well succeeded algorithms from the literature.

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Cited By

du Plessis MPillay N(2025)Evolving Velocity Equations for Particle Swarm Optimisation for Function ApproximationArtificial Intelligence and Soft Computing10.1007/978-3-031-84356-3_3(28-39)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-84356-3_3
da Silva CRosa DMiranda PSi TCerri RBasgalupp M(2024)Automated CNN optimization using multi-objective grammatical evolutionApplied Soft Computing10.1016/j.asoc.2023.111124151(111124)Online publication date: Jan-2024
https://doi.org/10.1016/j.asoc.2023.111124
Camacho-Villalon CDorigo MStutzle T(2022)PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.310286326:3(402-416)Online publication date: Jun-2022
https://doi.org/10.1109/TEVC.2021.3102863
Show More Cited By

GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

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Published: 11 July 2015

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CAPES
CNPq
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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

du Plessis MPillay N(2025)Evolving Velocity Equations for Particle Swarm Optimisation for Function ApproximationArtificial Intelligence and Soft Computing10.1007/978-3-031-84356-3_3(28-39)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-84356-3_3
da Silva CRosa DMiranda PSi TCerri RBasgalupp M(2024)Automated CNN optimization using multi-objective grammatical evolutionApplied Soft Computing10.1016/j.asoc.2023.111124151(111124)Online publication date: Jan-2024
https://doi.org/10.1016/j.asoc.2023.111124
Camacho-Villalon CDorigo MStutzle T(2022)PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.310286326:3(402-416)Online publication date: Jun-2022
https://doi.org/10.1109/TEVC.2021.3102863
Okulewicz MZaborski MMańdziuk J(2022)Self-Adapting Particle Swarm Optimization for continuous black box optimizationApplied Soft Computing10.1016/j.asoc.2022.109722131(109722)Online publication date: Dec-2022
https://doi.org/10.1016/j.asoc.2022.109722
Mısır M(2021)Hyper-heuristics: Autonomous Problem SolversAutomated Design of Machine Learning and Search Algorithms10.1007/978-3-030-72069-8_7(109-131)Online publication date: 29-Jul-2021
https://doi.org/10.1007/978-3-030-72069-8_7
Junior JAranha CSakurai T(2020)A Training Difficulty Schedule for Effective Search of Meta-Heuristic Design2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185806(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185806
Lima RPozo AMendiburu ASantana R(2020)A Symmetric grammar approach for designing segmentation models2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185760(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185760
Remes de Lima RPozo ASantana R(2019)Automatic Design of Convolutional Neural Networks using Grammatical Evolution2019 8th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2019.00065(329-334)Online publication date: Oct-2019
https://doi.org/10.1109/BRACIS.2019.00065
Miranda PPrudêncio R(2018)A novel context-free grammar for the generation of PSO algorithmsNatural Computing10.1007/s11047-018-9679-9Online publication date: 23-Mar-2018
https://doi.org/10.1007/s11047-018-9679-9
Melo VBanzhaf W(2018)Drone Squadron OptimizationNeural Computing and Applications10.1007/s00521-017-2881-330:10(3117-3144)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s00521-017-2881-3
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