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Multi-particle Simulated Annealing

  • Orcun Molvalioglu
  • Zelda B. Zabinsky
  • Wolf Kohn
Part of the Optimization and Its Applications book series (SOIA, volume 4)

Summary

Whereas genetic algorithms and evolutionary methods involve a population of points, simulated annealing (SA) can be interpreted as a random walk of a single point inside a feasible set. The sequence of locations visited by SA is a consequence of the Markov Chain Monte Carlo sampler. Instead of running SA with multiple independent runs, in this chapter we study a multi-particle version of simulated annealing in which the population of points interact with each other. We present numerical results that demonstrate the benefits of these interactions on algorithm performance.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Orcun Molvalioglu
    • 1
  • Zelda B. Zabinsky
    • 1
  • Wolf Kohn
    • 2
  1. 1.Industrial Engineering ProgramUniversity of WashingtonSeattleUSA
  2. 2.Clearsight Systems Inc.BellevueUSA

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