Performance Analysis of Cyclical Simulated Annealing Algorithms

Sheldon H. Jacobson (), Shane N. Hall (), Laura A. McLay () and Jeffrey E. Orosz ()
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Sheldon H. Jacobson: University of Illinois at Urbana-Champaign
Shane N. Hall: University of Illinois at Urbana-Champaign
Laura A. McLay: University of Illinois at Urbana-Champaign
Jeffrey E. Orosz: Ritchie Capital Management

Methodology and Computing in Applied Probability, 2005, vol. 7, issue 2, 183-201

Abstract: Abstract Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms for addressing intractable discrete optimization problems. Measures for assessing the finite-time performance of GHC algorithms have been developed using this framework, including the expected number of iterations to visit a predetermined objective function value level. This paper analyzes how the expected number of iterations to visit a predetermined objective function value level can be estimated for cyclical simulated annealing. Cyclical simulated annealing uses a cooling schedule that cycles through a set of temperature values. Computational results with traveling salesman problem instances taken from TSPLIB show how the expected number of iterations to visit solutions with predetermined objective function levels can be estimated for cyclical simulated annealing.

Keywords: local search algorithms; heuristics; simulated annealing; cooling schedules (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1007/s11009-005-1482-2

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