This paper presents a local energy distribution based hyperparameter determination for stochastic simulated annealing (SSA). SSA is capable of solving combinatorial optimization problems faster than typical simulated annealing (SA), but requires a time-consuming hyperparameter search. The proposed method determines hyperparameters based on the local energy distributions of spins (probabilistic bits). The spin is a basic computing element of SSA and is graphically connected to other spins with its weights. The distribution of the local energy can be estimated based on the central limit theorem (CLT). The CLT-based normal distribution is used to determine the hyperparameters, which reduces the time complexity for hyperparameter search from O(n^3) of the conventional method to O(1). The performance of SSA with the determined hyperparameters is evaluated on the Gset and K2000 benchmarks for maximum-cut problems. The results show that the proposed method achieves mean cut values of approximately 98% of the best-known cut values.

翻译：本文提出了一种基于局部能量分布的随机模拟退火（SSA）超参数确定方法。SSA能够比典型模拟退火（SA）更快地解决组合优化问题，但需要耗时的超参数搜索。所提方法基于自旋（概率比特）的局部能量分布确定超参数。自旋是SSA的基本计算单元，通过其权重与其他自旋形成图连接。基于中心极限定理（CLT）可估计局部能量分布。利用基于CLT的正态分布确定超参数，将超参数搜索的时间复杂度从传统方法的O(n³)降低至O(1)。在最大割问题的Gset和K2000基准测试上评估了所确定超参数下SSA的性能。结果表明，所提方法实现的平均割值达到已知最优割值的约98%。