Simulated Annealing

Alexander G. Nikolaev () and Sheldon H. Jacobson ()
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Alexander G. Nikolaev: University at Buffalo
Sheldon H. Jacobson: University of Illinois

Chapter Chapter 1 in Handbook of Metaheuristics, 2010, pp 1-39 from Springer

Abstract: 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.

Keywords: Simulated Annealing; Tabu Search; Travel Salesman Problem; Simulated Annealing Algorithm; Discrete Optimization Problem (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-1665-5_1

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