Analysis of static simulated annealing algorithms

Authors:
J. E. Orosz;S. H. Jacobson
Affiliations:
Research Contractor, Materials Process Design Branch, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio;Professor and Director, Simulation and Optimization Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois, Urbana, Illinois
Venue:
Journal of Optimization Theory and Applications
Year:
2002

Citing 11
Cited 3

Cooling schedules for optimal annealing

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A Note on the Finite Time Behavior of Simulated Annealing

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On the convergence of generalized hill climbing algorithms

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Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Finite-Time Performance Analysis of Static Simulated Annealing Algorithms

Computational Optimization and Applications
Simulated Annealing: Searching for an Optimal Temperature Schedule

SIAM Journal on Optimization
Simulated Annealing With An Optimal Fixed Temperature

SIAM Journal on Optimization
Analysis of static simulated annealing algorithms

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A class of convergent generalized hill climbing algorithms

Applied Mathematics and Computation
Information Theory and the Finite-Time Behavior of the Simulated Annealing Algorithm: Experimental Results

INFORMS Journal on Computing
A convergence analysis of generalized hill climbing algorithms

A convergence analysis of generalized hill climbing algorithms

Analysis of static simulated annealing algorithms

Journal of Optimization Theory and Applications
Global Optimization Performance Measures for Generalized Hill Climbing Algorithms

Journal of Global Optimization
A fuzzy adaptive turbulent particle swarm optimisation

International Journal of Innovative Computing and Applications

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Abstract

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms to address intractable discrete optimization problems. This paper introduces a measure for determining the expected number of iterations to visit a predetermined objective function level, given that an inferior objective function level has been reached in a finite number of iterations. A variation of simulated annealing (SA), termed static simulated annealing (S2A), is analyzed using this measure. S2A uses a fixed cooling schedule during the algorithm execution. Though S2A is probably nonconvergent, its finite-time performance can be assessed using the finite-time performance measure defined in this paper.