论文标题
热循环算法的优化和基准测试
Optimization and benchmarking of the thermal cycling algorithm
论文作者
论文摘要
优化在许多科学和技术领域都起着重要作用。大多数工业优化问题都具有不合别的复杂结构，使其将其全球最小化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，我们基准并改进热循环算法[Phys。莱特牧师。 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屏障。我们对算法进行全面的参数调整，并证明它与其他最先进的算法紧密竞争，例如使用同盟群集移动的平行回火，同时压倒性地超过了更简单的启发式方法，例如模拟退火。
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.
