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Simulated Annealing

In: Discrete Diversity and Dispersion Maximization

Author

Listed:
  • Rex K. Kincaid

    (William & Mary)

  • Anh Ninh

    (William & Mary)

Abstract

Simulated annealing (SA) is a well-known local search metaheuristic for finding high-quality solutions to both discrete and continuous optimization problems. The algorithm was first proposed and used in statistical mechanics by Metropolis et al. (J Chem Phys 21:1087-1092, 1953). Yet, not until Kirkpatrick et al. (Science 220:671-680, 1983) and Cerny (J Optim Theory Appl 45:41–52, 1985) was SA implemented as a heuristic for a notoriously hard combinatorial optimization problem – the traveling salesman problem. Since then, SA has been successfully applied across a broad range of application areas such as finance, machine learning, operations research, etc., where the associated optimization problems can be computationally intractable for large problem instances. In these situations, SA is a serious contender as a convenient, yet effective, optimization tool as it requires no knowledge of the problem structure. Indeed, the key advantage of SA is in its simplicity, which facilitates quick implementation for solving many real-life applications.

Suggested Citation

  • Rex K. Kincaid & Anh Ninh, 2023. "Simulated Annealing," Springer Optimization and Its Applications, in: Rafael Martí & Anna Martínez-Gavara (ed.), Discrete Diversity and Dispersion Maximization, chapter 0, pages 221-249, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-38310-6_10
    DOI: 10.1007/978-3-031-38310-6_10
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