Incremental Particle Swarm-Guided Local Search for Continuous Optimization

  • Marco A. Montes de Oca
  • Ken Van den Enden
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5296)


We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provides rapid convergence to good solutions while the particle swarm algorithm enables a comprehensive exploration of the search space. We provide experimental evidence that shows that the algorithm can find good solutions very rapidly without compromising its global search capabilities.


Particle Swarm Optimization Local Search Particle Swarm Social Learning Multiagent System 
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 2008

Authors and Affiliations

  • Marco A. Montes de Oca
    • 1
  • Ken Van den Enden
    • 2
  • Thomas Stützle
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Vrije Universiteit BrusselBrusselsBelgium

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