Simulated Annealing

  • Sun-Chong Wang
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 743)


In many applications, the parameters which are sought to minimize (or maximize) the objective function are not continuously varying. For instance, a salesperson is to travel through a series of cities in an order that gives the shortest traveling distance. The parameters take the form of integers in this case. And as the number of cities increases, the number of possible configurations (sequences) grows rapidly, rendering exhaustive search infeasible.


Simulated Annealing Ising Model Travel Salesman Problem Metropolis Algorithm Dimensional Array 
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References and Further Reading

  1. The 1-dimensional Ising model was solved in, E. Ising, “Beitrag zur theorie des ferromagnetismus”, Zeits. fur Phys., 31 (1925) 253–258CrossRefGoogle Scholar
  2. Theories of equilibrium thermodynamics can be found in the textbook, K, Huang, “Statistical Mechanics”, John Wiley & Sons, New York (1987)zbMATHGoogle Scholar
  3. Simulated annealing and thermodynamics were discussed in, S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, “Optimization by Simulated Annealing”, Science, 220 (1983) 671–680MathSciNetzbMATHCrossRefGoogle Scholar
  4. The Metropolis algorithm appeared in, N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, E.H. Teller, and E. Teller, “Equation of State Calculations by Fast Computing Machines”, Journal of Chemical Physics, 21 (1953) 1087–1091CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Sun-Chong Wang
    • 1
  1. 1.TRIUMFCanada

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