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Community Swarm Optimization

  • Rawan Ghnemat
  • Cyrille Bertelle
  • Gérard H. E. Duchamp
Part of the Understanding Complex Systems book series (UCS)

Summary

The development of distributed computations and complex systems modelling [11] leads to the creation of innovative algorithms based on interacting virtual entities, specifically for optimisation purposes within complex phenomena. Particule Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) are two of these algorithms. We propose in this paper a method called Community Swarm Optimisation (CSO). This method is based on more sophisticated entities which are defined by behavioral automata. This algorithm leads to the emergence of the solution by the co-evolution of their behavioral and spatial characteristics. This method is suitable for urban management, in order to improve the understanding of the individual behaviors over the emergent urban organizations.

Keywords

swarm optimisation community dectection self-organization automata evolutive methods geographic systems ressource management 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rawan Ghnemat
    • 1
  • Cyrille Bertelle
    • 1
  • Gérard H. E. Duchamp
    • 2
  1. 1.LITISUniversity of Le HavreLe Havre cedexFrance
  2. 2.LIPNUniversity of Paris XIIIVilletaneuseFrance

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