Optimization plays a significant role in many areas of science and technology. Most of the industrial optimization problems have inordinately complex structures that render finding their global minima a daunting task. Therefore, designing heuristics that can efficiently solve such problems is of utmost importance. In this paper we benchmark and improve the thermal cycling algorithm [Phys. Rev. Lett. 79, 4297 (1997)] that is designed to overcome energy barriers in nonconvex optimization problems by temperature cycling of a pool of candidate solutions. We perform a comprehensive parameter tuning of the algorithm and demonstrate that it competes closely with other state-of-the-art algorithms such as parallel tempering with isoenergetic cluster moves, while overwhelmingly outperforming more simplistic heuristics such as simulated annealing.

中文翻译：

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热循环算法的优化和基准测试

优化在许多科学和技术领域发挥着重要作用。大多数工业优化问题具有极其复杂的结构，这使得找到其全局最小值成为一项艰巨的任务。因此，设计可以有效解决此类问题的启发式方法至关重要。在本文中，我们对热循环算法进行了基准测试和改进 [ Phys. 牧师莱特。 79, 4297 (1997)] 旨在通过候选解决方案池的温度循环来克服非凸优化问题中的能量障碍。我们对算法进行了全面的参数调整，并证明它与其他最先进的算法（例如具有等能集群移动的并行调和）密切竞争，同时压倒性地优于更简单的启发式算法（例如模拟退火）。