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Simulated Annealing

  • Kathryn A. Dowsland
  • Jonathan M. Thompson

Abstract

Since its introduction as a generic heuristic for discrete optimization in 1983, simulated annealing (SA) has become a popular tool for tackling both discrete and continuous problems across a broad range of application areas. This chapter provides an overview of the technique with the emphasis being on the use of simulated annealing in the solution of practical problems. A detailed statement of the algorithm is given, together with an explanation of its inspiration from the field of statistical thermodynamics. This is followed by a brief overview of the theory with emphasis on those results that are important to the decisions that need to be made for a practical implementation. It then goes on to look at some of the ways in which the basic algorithm has been modified in order to improve its performance in the solution of a variety of problems. It also includes a brief section on application areas and concludes with general observations and pointers to other sources of information such as survey articles and websites offering downloadable simulated annealing code.

Keywords

Local Search Simulated Annealing Solution Space Travel Salesman Problem Combinatorial Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Gower Optimal Algorithms, Ltd.SwanseaUK
  2. 2.School of MathematicsCardiff UniversityCardiffUK

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