A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

LaTorre de la Fuente, Antonio; Muelas Pascual, Santiago y Peña Sanchez, Jose Maria (2010). A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test. "Soft Computing - A Fusion of Foundations, Methodologies and Applications", v. 15 ; https://doi.org/10.1007/s00500-010-0646-3.

Descripción

Título: A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test
Autor/es:
  • LaTorre de la Fuente, Antonio
  • Muelas Pascual, Santiago
  • Peña Sanchez, Jose Maria
Tipo de Documento: Artículo
Título de Revista/Publicación: Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fecha: Septiembre 2010
Volumen: 15
Materias:
Palabras Clave Informales: Continuous optimization - Multiple offspring sampling - Scalability
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue.

Más información

ID de Registro: 7247
Identificador DC: http://oa.upm.es/7247/
Identificador OAI: oai:oa.upm.es:7247
Identificador DOI: 10.1007/s00500-010-0646-3
URL Oficial: http://www.springerlink.com/content/w7x938v8w28w98jn/
Depositado por: Memoria de Investigacion 2
Depositado el: 30 May 2011 08:21
Ultima Modificación: 20 Abr 2016 16:25
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