During the last decades many metaheuristics for global numerical optimization have been proposed. Among them, Basin Hopping is very simple and straightforward to implement, although rarely used outside its original Physical Chemistry community. In this work, our aim is to compare Basin Hopping, and two population variants of it, with readily available implementations of the well known metaheuristics Differential Evolution, Particle Swarm Optimization, and Covariance Matrix Adaptation Evolution Strategy. We perform numerical experiments using the IOH profiler environment with the BBOB test function set and two difficult real-world problems. The experiments were carried out in two different but complementary ways: by measuring the performance under a fixed budget of function evaluations and by considering a fixed target value. The general conclusion is that Basin Hopping and its newly introduced population variant are almost as good as Covariance Matrix Adaptation on the synthetic benchmark functions and better than it on the two hard cluster energy minimization problems. Thus, the proposed analyses show that Basin Hopping can be considered a good candidate for global numerical optimization problems along with the more established metaheuristics, especially if one wants to obtain quick and reliable results on an unknown problem.

翻译：在过去几十年中，许多用于全局数值优化的元启发式方法被提出。其中，盆跳跃方法实现简单且直接，尽管在其原始物理化学社区之外很少使用。本研究的目的是将盆跳跃方法及其两种种群变体，与已知元启发式方法（差分进化、粒子群优化和协方差矩阵自适应进化策略）的现有实现进行比较。我们使用IOH分析器环境，在BBOB测试函数集和两个困难的实际问题上进行数值实验。实验通过两种不同但互补的方式进行：在固定函数评估预算下测量性能，以及考虑固定目标值。总体结论是，盆跳跃方法及其新引入的种群变体在合成基准函数上的表现几乎与协方差矩阵自适应方法相当，并在两个困难的簇能量最小化问题上优于它。因此，所提出的分析表明，盆跳跃方法可被视为全局数值优化问题的一个良好候选，与更成熟的元启发式方法并列，特别是当人们希望在未知问题上快速获得可靠结果时。