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Content Provider | IEEE Xplore Digital Library |
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Author | Liang Zhang Ling Wang |
Copyright Year | 2003 |
Description | Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Liang Zhang; Ling Wang) |
Abstract | Simulated annealing (SA) is a stochastic global search approach, which is of the ability to escape from local minima with deteriorative accepted probability and has been successfully applied to many difficult combinatorial and numerical optimization problems. But it is well known that the performance of SA highly depends on its parameters, especially the annealing schedule. Traditionally, the parameters of SA are determined empirically or by trial and error. In this paper, the determination of optimal SA parameters with limited computation effort is viewed as a stochastic problem, and then a systematic procedure based on ordinal optimization (OO) and optimal computing budget allocation (OCBA) is applied to select the most reasonable parameter combination. Simulation results based on flow shop scheduling benchmarks demonstrate the effectiveness. |
Sponsorship | IEEE Circuits and Syst. Soc. (CASS) IEEE R10 CASS IEEE Beijing Section Chinese Neural Networks Council Circuits and Information Processing Committee, CASS-CIE Southeast Univ., China |
Starting Page | 412 |
Ending Page | 415 |
File Size | 290113 |
Page Count | 4 |
File Format | |
ISBN | 0780377028 |
DOI | 10.1109/ICNNSP.2003.1279296 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2003-12-14 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Temperature distribution Temperature dependence Job shop scheduling Automation Processor scheduling Computational modeling Stochastic systems Stochastic processes Simulated annealing Space exploration |
Content Type | Text |
Resource Type | Article |
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