Evolving the Structure of the Particle Swarm Optimization Algorithms

  • Laura Dioşan
  • Mihai Oltean
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)


A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the evolved PSO algorithm performs similarly and sometimes even better than standard approaches for the considered problems.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Cardinality Constraint Portfolio Selection Problem Standard Particle Swarm Optimization 
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 2006

Authors and Affiliations

  • Laura Dioşan
    • 1
  • Mihai Oltean
    • 1
  1. 1.Department of Computer Science, Faculty of Mathematics and Computer ScienceBabeş-Bolyai UniversityCluj-NapocaRomania

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