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Simulated Annealing

In: Handbook of Metaheuristics

Author

Listed:
  • Alexander G. Nikolaev

    (University at Buffalo)

  • Sheldon H. Jacobson

    (University of Illinois)

Abstract

Simulated annealing is a well-studied local search metaheuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a mechanism to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a global optimum. A brief history of simulated annealing is presented, including a review of its application to discrete, continuous, and multi-objective optimization problems. Asymptotic convergence and finite-time performance theory for simulated annealing are reviewed. Other local search algorithms are discussed in terms of their relationship to simulated annealing. The chapter also presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications.

Suggested Citation

  • Alexander G. Nikolaev & Sheldon H. Jacobson, 2010. "Simulated Annealing," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 1-39, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_1
    DOI: 10.1007/978-1-4419-1665-5_1
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    Cited by:

    1. Bongiovanni, Claudia & Kaspi, Mor & Cordeau, Jean-François & Geroliminis, Nikolas, 2022. "A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).

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