Hybrid Particle Swarm – Evolutionary Algorithm for Search and Optimization

  • Crina Grosan
  • Ajith Abraham
  • Sangyong Han
  • Alexander Gelbukh
Conference paper

DOI: 10.1007/11579427_63

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3789)
Cite this paper as:
Grosan C., Abraham A., Han S., Gelbukh A. (2005) Hybrid Particle Swarm – Evolutionary Algorithm for Search and Optimization. In: Gelbukh A., de Albornoz Á., Terashima-Marín H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science, vol 3789. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Crina Grosan
    • 1
  • Ajith Abraham
    • 2
  • Sangyong Han
    • 2
  • Alexander Gelbukh
    • 3
  1. 1.Department of Computer ScienceBabeş-Bolyai UniversityCluj-NapocaRomania
  2. 2.School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  3. 3.Centro de Investigación en Computación (CIC)Instituto Politécnico Nacional (IPN)Mexico

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