Parallelizing simulated annealing algorithms based on high-performance computer

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We implemented five conversions of simulated annealing (SA) algorithm from sequential-to-parallel forms on high-performance computers and applied them to a set of standard function optimization problems in order to test their performances. According to the experimental results, we eventually found that the traditional approach to parallelizing simulated annealing, namely, parallelizing moves in sequential SA, difficultly handled very difficult problem instances. Divide-and-conquer decomposition strategy used in a search space sometimes might find the global optimum function value, but it frequently resulted in great time cost if the random search space was considerably expanded. The most effective way we found in identifying the global optimum solution is to introduce genetic algorithm (GA) and build a highly hybrid GA+SA algorithm. In this approach, GA has been applied to each cooling temperature stage. Additionally, the performance analyses of the best algorithm among the five implemented algorithms have been done on the IBM Beowulf PCs Cluster and some comparisons have been made with some recent global optimization algorithms in terms of the number of functional evaluations needed to obtain a global minimum, success rate and solution quality.
Publisher
Springer
Issue Date
2007-01
Language
English
Article Type
Article
Keywords

CONTINUOUS-VARIABLES; OPTIMIZATION

Citation

JOURNAL OF GLOBAL OPTIMIZATION, v.39, no.2, pp.261 - 289

ISSN
0925-5001
DOI
10.1007/s10898-007-9138-0
URI
http://hdl.handle.net/10203/89993
Appears in Collection
EE-Journal Papers(저널논문)
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