Hybrid particle swarm - Evolutionary algorithm for search and optimization

Crina Grosan, Ajith Abraham, Sangyong Han, Alexander Gelbukh

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

9 Citas (Scopus)

Resumen

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO - evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

Idioma originalInglés
Título de la publicación alojadaMICAI 2005
Subtítulo de la publicación alojadaAdvances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings
Páginas623-632
Número de páginas10
DOI
EstadoPublicada - 2005
Evento4th Mexican International Conference on Artificial Intelligence, MICAI 2005 - Monterrey, México
Duración: 14 nov. 200518 nov. 2005

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen3789 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia4th Mexican International Conference on Artificial Intelligence, MICAI 2005
País/TerritorioMéxico
CiudadMonterrey
Período14/11/0518/11/05

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