Efficient Differential Grouping Method for Large-scale Constrained Optimization

Solving large-scale constrained optimization problems (LSCOP) using cooperative co-evolutionary (CC) algorithms has received considerable attention in recent years. One of the most critical challenges of CC algorithms is the decomposition of the original problem. Currently, decomposition methods for...

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Autores principales: Paz, Fabiola, Leguizamón, Guillermo, Mezura Montes, Efrén
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Ciencias Informáticas
Differential Grouping
Interaction Between Variables
Decomposition Methods
Cooperative Co-evolution Algorithms
Large-scale Constrained Optimization
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/176204
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SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-176204
record_format dspace
spelling I19-R120-10915-1762042025-02-06T20:05:37Z http://sedici.unlp.edu.ar/handle/10915/176204 Efficient Differential Grouping Method for Large-scale Constrained Optimization Paz, Fabiola Leguizamón, Guillermo Mezura Montes, Efrén 2024-10 2024 2025-02-06T13:38:02Z en Ciencias Informáticas Differential Grouping Interaction Between Variables Decomposition Methods Cooperative Co-evolution Algorithms Large-scale Constrained Optimization Solving large-scale constrained optimization problems (LSCOP) using cooperative co-evolutionary (CC) algorithms has received considerable attention in recent years. One of the most critical challenges of CC algorithms is the decomposition of the original problem. Currently, decomposition methods for solving LSCOPs often generate large subcomponents, poor accuracy, and consume significant computational resources. This affects the optimization performance. In this work, we present a decomposition method that aims to form efficient subcomponents that feature high accuracy, good size, and low computational resource cost. The proposal is evaluated on a set of benchmark functions subject to constraints up to 1,000 dimensions widely used in the literature, and compared with other state-of-the-art methods. The results demonstrate that our proposal performs the decomposition efficiently and succeeds in reducing the computational cost, making it a valuable contribution to the field of optimization. Red de Universidades con Carreras en Informática Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 54-63
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Differential Grouping
Interaction Between Variables
Decomposition Methods
Cooperative Co-evolution Algorithms
Large-scale Constrained Optimization
spellingShingle Ciencias Informáticas
Differential Grouping
Interaction Between Variables
Decomposition Methods
Cooperative Co-evolution Algorithms
Large-scale Constrained Optimization
Paz, Fabiola
Leguizamón, Guillermo
Mezura Montes, Efrén
Efficient Differential Grouping Method for Large-scale Constrained Optimization
topic_facet Ciencias Informáticas
Differential Grouping
Interaction Between Variables
Decomposition Methods
Cooperative Co-evolution Algorithms
Large-scale Constrained Optimization
description Solving large-scale constrained optimization problems (LSCOP) using cooperative co-evolutionary (CC) algorithms has received considerable attention in recent years. One of the most critical challenges of CC algorithms is the decomposition of the original problem. Currently, decomposition methods for solving LSCOPs often generate large subcomponents, poor accuracy, and consume significant computational resources. This affects the optimization performance. In this work, we present a decomposition method that aims to form efficient subcomponents that feature high accuracy, good size, and low computational resource cost. The proposal is evaluated on a set of benchmark functions subject to constraints up to 1,000 dimensions widely used in the literature, and compared with other state-of-the-art methods. The results demonstrate that our proposal performs the decomposition efficiently and succeeds in reducing the computational cost, making it a valuable contribution to the field of optimization.
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 Efficient Differential Grouping Method for Large-scale Constrained Optimization
title_short Efficient Differential Grouping Method for Large-scale Constrained Optimization
title_full Efficient Differential Grouping Method for Large-scale Constrained Optimization
title_fullStr Efficient Differential Grouping Method for Large-scale Constrained Optimization
title_full_unstemmed Efficient Differential Grouping Method for Large-scale Constrained Optimization
title_sort efficient differential grouping method for large-scale constrained optimization
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/176204
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AT leguizamonguillermo efficientdifferentialgroupingmethodforlargescaleconstrainedoptimization
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