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The Theory and Practice of Simulated Annealing

  • Darrall Henderson
  • Sheldon H. Jacobson
  • Alan W. Johnson
Part of the International Series in Operations Research & Management Science book series (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.

Keywords

Local Search Algorithms Simulated Annealing Heuristics Meta-heuristics 

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Darrall Henderson
    • 1
  • Sheldon H. Jacobson
    • 2
  • Alan W. Johnson
    • 3
  1. 1.Department of Mathematical SciencesUnited States Military AcademyWest PointUSA
  2. 2.Department of Mechanical and Industrial EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Department of Mathematical SciencesUnited States Military AcademyWest PointUSA

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