Solving Quadratic Assignment Problems by ‘Simulated Annealing’
作者:
MickeyR. Wilhelm,
ThomasL. Ward,
期刊:
IIE Transactions
(Taylor Available online 1987)
卷期:
Volume 19,
issue 1
页码: 107-119
ISSN:0740-817X
年代: 1987
DOI:10.1080/07408178708975376
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Recently, an interesting analogy between problems in combinatorial optimization and statistical mechanics has been developed and has proven useful in solving certain traditional optimization problems such as computer design, partitioning, component placement, wiring, and traveling salesman problems. The analogy has resulted in a methodology, termed “simulated annealing,” which, in the process of iterating to an optimum, uses Monte Carlo sampling to occasionally accept solutions to discrete optimization problems which increase rather than decrease the objective function value. This process is counter to the normal ‘steepest-descent’ algorithmic approach. However, it is argued in the analogy that by taking such controlled uphill steps, the optimizing algorithm need not get “stuck” on inferior solutions.
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