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Multi-particle Simulated Annealing

In: Models and Algorithms for Global Optimization

Author

Listed:
  • Orcun Molvalioglu

    (University of Washington)

  • Zelda B. Zabinsky

    (University of Washington)

  • Wolf Kohn

    (Clearsight Systems Inc.)

Abstract

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.

Suggested Citation

  • Orcun Molvalioglu & Zelda B. Zabinsky & Wolf Kohn, 2007. "Multi-particle Simulated Annealing," Springer Optimization and Its Applications, in: Aimo Törn & Julius Žilinskas (ed.), Models and Algorithms for Global Optimization, pages 215-222, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-36721-7_14
    DOI: 10.1007/978-0-387-36721-7_14
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    Cited by:

    1. Enlu Zhou & Xi Chen, 2013. "Sequential Monte Carlo simulated annealing," Journal of Global Optimization, Springer, vol. 55(1), pages 101-124, January.

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