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.
Original language | English (US) |
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Pages (from-to) | 165-182 |
Number of pages | 18 |
Journal | Journal of Optimization Theory and Applications |
Volume | 115 |
Issue number | 1 |
DOIs | |
State | Published - Oct 2002 |
Keywords
- Local search algorithms
- cooling schedules
- finite-time performance
- simulated annealing
ASJC Scopus subject areas
- Management Science and Operations Research
- Control and Optimization
- Applied Mathematics