Hybrid Particle Swarm – Evolutionary Algorithm for Search and Optimization

  • Crina Grosan
  • Ajith Abraham
  • Sangyong Han
  • Alexander Gelbukh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3789)


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.


Evolutionary Algorithm Particle Swarm Particle Swarm Optimization Algorithm Geometrical Place Hybrid Particle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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