论文标题
元启发式学值得吗？关于黑盒优化问题的自然风格和确定性技术之间的计算比较
Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems
论文作者
论文摘要
在无衍生优化的领域，其两个主要分支，即确定性和自然风格的技术，近年来经历了很大的进步。在本文中，我们对这些分支中的每个分支中选定的方法进行了广泛的计算比较。所选的代表是标准和利用良好的方法，或者是最近数值比较的表现最佳的方法。计算比较是在五个不同的基准集上进行的，并根据所选方法的性能，时间复杂性和收敛属性进行了分析。结果表明，在处理目标函数评估相对便宜的情况时，自然风格的方法的性能明显优于确定性的方法。但是，在函数评估成本高昂或禁止的情况下，确定性方法可能会提供更一致和总体上更好的结果。
In the field of derivative-free optimization, both of its main branches, the deterministic and nature-inspired techniques, experienced in recent years substantial advancement. In this paper, we provide an extensive computational comparison of selected methods from each of these branches. The chosen representatives were either standard and well-utilized methods, or the best-performing methods from recent numerical comparisons. The computational comparison was performed on five different benchmark sets and the results were analyzed in terms of performance, time complexity, and convergence properties of the selected methods. The results showed that, when dealing with situations where the objective function evaluations are relatively cheap, the nature-inspired methods have a significantly better performance than their deterministic counterparts. However, in situations when the function evaluations are costly or otherwise prohibited, the deterministic methods might provide more consistent and overall better results.
