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Increasing the Efficiency of Simulated Annealing Search by Learning to Recognize (Un)Promising Runs
Any type of content formally published in an academic journal, usually following a peer-review process.
posted on 01.01.1994by Yoichiro Nakakuki, Norman Sadeh
Simulatd Annealing (SA) procedures can potentially yield near-optimal solutions
to many difficult combinatorial optimization problems, though often at
the expense of intensive computational efforts. The single most significant source
of inefficiency in SA search is the inherent stochasticity of the procedure typically
requiring that the procedure be rerun a large number of times before a
near-optimal solution is found. This paper describes a mechanism that atteinpls
to learn the structure of the search space over multiple SA runs on a given problem.
Specifically, probability distributions are dynamically updated over multiple
runs to estimate at different checkpoints how promising a SA run appears to be.
Based on this mechanism, two types of criteria are developed that aim at increasing
search efficiency: (1) a cutoff criterion used to determine when to abandon
unpromising runs and (2) restart criteria used to determine whether to start a
fresh SA run or restart search in the middle of an earlier run. Experimental results
obtained on a class of complex job shop scheduling problems show (1) that
SA can produce high quality solutions for this class of problems, if run a large
number of times, and (2) that our learning mechanism can significantly reduce
the computation time required to find high quality solutions to these problems.
The results further indicate that, the closer one wants to be to the optimum, the
larger the speedups.