@inproceedings{6d0e3fdc0b3a45c9a9ac2aa32a81ecd7,
title = "Hybrid particle swarm - Evolutionary algorithm for search and optimization",
abstract = "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.",
author = "Crina Grosan and Ajith Abraham and Sangyong Han and Alexander Gelbukh",
year = "2005",
doi = "10.1007/11579427_63",
language = "Ingl{\'e}s",
isbn = "3540298967",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "623--632",
booktitle = "MICAI 2005",
note = "4th Mexican International Conference on Artificial Intelligence, MICAI 2005 ; Conference date: 14-11-2005 Through 18-11-2005",
}