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arXiv · 2012.09801

Optimization and benchmarking of the thermal cycling algorithm

Amin Barzegar·Anuj Kankani·Salvatore Mandrà·Helmut G. Katzgraber
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Abstract

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.

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Amin Barzegar, Anuj Kankani, Salvatore Mandrà, Helmut G. Katzgraber. 2020-12-17. Optimization and benchmarking of the thermal cycling algorithm. https://doi.org/10.1103/physreve.104.035302

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