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The Theory and Practice of Simulated Annealing

  • Chapter
Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 57))

Abstract

Simulated annealing is a popular local search meta-heuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a means 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 and continuous optimization problems. Convergence theory for simulated annealing is reviewed, as well as recent advances in the analysis of finite time performance. 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.

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Authors and Affiliations

  1. Department of Mathematical Sciences, United States Military Academy, West Point, NY, 10996-1786, USA

    Darrall Henderson

  2. Department of Mechanical and Industrial Engineering, University of Illinois at Urbana-Champaign, 1206 West Green Street, MC-244, Urbana, Illinois, 61801-2906, USA

    Sheldon H. Jacobson

  3. Department of Mathematical Sciences, United States Military Academy, West Point, NY, 10996-1786, USA

    Alan W. Johnson

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  1. Darrall Henderson
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Editors and Affiliations

  1. Leeds School of Business, University of Colorado at Boulder, USA

    Fred Glover

  2. College of Business, University of Colorado at Denver, USA

    Gary A. Kochenberger

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Henderson, D., Jacobson, S.H., Johnson, A.W. (2003). The Theory and Practice of Simulated Annealing. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 57. Springer, Boston, MA. https://doi.org/10.1007/0-306-48056-5_10

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