This paper presents a parallel simulated annealing algorithm that is able to achieve 90% parallel efficiency in iteration on up to 192 processors and up to 40% parallel efficiency in time when applied to a 5000-dimension Rastrigin function. Our algorithm breaks scalability barriers in the method of Chu et al. (1999) by abandoning adaptive cooling based on variance. The resulting gains in parallel efficiency are much larger than the loss of serial efficiency from lack of adaptive cooling. Our algorithm resamples the states across processors periodically. The resampling interval is tuned according to the success rate for each specific number of processors. We further present an adaptive method to determine the resampling interval based on the adoption rate. This adaptive method is able to achieve nearly identical parallel efficiency but higher success rates compared to the fixed interval one using the best interval found.

中文翻译：

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使用自适应重采样间隔的并行模拟退火。

本文提出了一种并行模拟退火算法，该算法能够在多达192个处理器上实现90％的并行并行效率，并在应用于5000维Rastrigin函数时在时间上达到40％的并行效率。我们的算法突破了Chu等人的方法的可扩展性壁垒。（1999）放弃了基于方差的自适应冷却。由于缺乏自适应冷却，并行效率的提高要远远大于串行效率的降低。我们的算法会定期对处理器之间的状态进行重新采样。根据每个特定数量的处理器的成功率来调整重采样间隔。我们进一步提出了一种基于采用率来确定重采样间隔的自适应方法。