A parallel statistical cooling algorithm
Statistical Cooling is a new optimization technique based on Monte-Carlo iterative improvement. Here we propose a parallel formulation of the statistical cooling algorithm based on the requirement that quasi-equilibrium is preserved throughout the optimization process. It is shown that the parallel algorithm can be executed in polynomial time. Performance of the algorithm is discussed by means of an implementation on an experimental multi-processor architecture. It is concluded that substantial reductions of computation time can be achieved by the parallel algorithm in comparison with the sequential algorithm.
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