Multi-particle Simulated Annealing

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


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AV94]
    Aldous, D., Vazirani, U.: Go with the winners algorithms. Proc. 35th Symp. Foundations of Computer Sci., pp. 492–501 (1994)Google Scholar
  2. [HS88]
    Holley, R., Stroock, D.: Simulated annealing via Sobolev inequalities. Comm. Math Phys., 115, 553–569 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  3. [KGV83]
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science, 220, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  4. [Loc00]
    Locatelli, M.: Simulated annealing algorithms for continuous global optimization:convergence conditions. Journal of Optimization Theory and Applications, 104(1), 121–133 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  5. [MM99]
    Moral, P.D., Miclo, L.: On the convergence and applications of generalized simulated annealing. Comm. Math Phys., 37(4), 1222–1250 (1999)zbMATHGoogle Scholar
  6. [Mor04]
    Moral, P.D.: Feynman-Kac Formulae: Genological and Interacting Particle Systems with Applications. Springer-Verlag, New York (2004)Google Scholar
  7. [NP95]
    Niemiro, W., Pokarowki, P.: Tail events of some nonhomogeneous Markov chains. The Annals of Applied Probability, 5(1), 261–293 (1995)zbMATHMathSciNetGoogle Scholar
  8. [RS94]
    Romeijn, H.E., Smith, R.L.: Simulated annealing and adaptive search in global optimization. Probability in the Engineering and Informational Science, 8, 571–590 (1994)CrossRefGoogle Scholar
  9. [Smi84]
    Smith, R.L.: Efficient Monte Carlo procedures for generating points uniformly distributed over bounded region. Operations Research, 32, 1296–1308 (1984)zbMATHMathSciNetCrossRefGoogle Scholar
  10. [SNT97]
    Sadeh, N.M., Nakakuki, Y., Thangiah, S.R.: Learning to recognize (un)promising simulated annealing runs:efficient search procedures for job shop scheduling and vehicle routing. Ann. Oper. Res., 75, 189–208 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  11. [Zab03]
    Zabinsky, Z.B.: Stochastic Adaptive Search for Global Optimization. Kluwer, Boston (2003)zbMATHGoogle Scholar

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

Personalised recommendations