  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.
