Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems

In this paper an alternative approach is proposed to improve the convergence of Particle Swarm Optimization (PSO) algorithm by adapting the inertial weight parameter with a fuzzy logic system to solve large-scale optimization problems. The PSO algorithm is a population-based metaheuristic inspired b...

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Detalles Bibliográficos
Autores principales: Paz, Fabiola, Leguizamón, Guillermo, Mezura-Montes, Efrén
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2020
Materias:
Ciencias Informáticas
Fuzzy logic
Particle Swarm Optimization
Convergence control
Adaptive inertia weight
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/113264
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-113264
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Fuzzy logic
Particle Swarm Optimization
Convergence control
Adaptive inertia weight
spellingShingle Ciencias Informáticas
Fuzzy logic
Particle Swarm Optimization
Convergence control
Adaptive inertia weight
Paz, Fabiola
Leguizamón, Guillermo
Mezura-Montes, Efrén
Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems
topic_facet Ciencias Informáticas
Fuzzy logic
Particle Swarm Optimization
Convergence control
Adaptive inertia weight
description In this paper an alternative approach is proposed to improve the convergence of Particle Swarm Optimization (PSO) algorithm by adapting the inertial weight parameter with a fuzzy logic system to solve large-scale optimization problems. The PSO algorithm is a population-based metaheuristic inspired by the social behavior of birds, and it has been applied to numerous optimization problems successfully. However, one of its main disadvantages is the decaying performance when applied to complex and large-scale problems. The proposed algorithm uses the fuzzy system to dynamically calculate a value of the Inertia Weight parameter during the search process to find better solutions. After carrying out experiments on a well-known benchmark for large-scale optimization, the proposed approach provides a competitive performance.
format Objeto de conferencia
Objeto de conferencia
author Paz, Fabiola
Leguizamón, Guillermo
Mezura-Montes, Efrén
author_facet Paz, Fabiola
Leguizamón, Guillermo
Mezura-Montes, Efrén
author_sort Paz, Fabiola
title Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems
title_short Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems
title_full Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems
title_fullStr Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems
title_full_unstemmed Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems
title_sort particle swarm optimization with adaptive inertia weight using fuzzy logic for large-scale problems
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/113264
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AT mezuramontesefren particleswarmoptimizationwithadaptiveinertiaweightusingfuzzylogicforlargescaleproblems
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