Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

中文翻译：

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使用机器学习技术的自适应布谷鸟搜索算法

元启发式是智能的问题解决者，在解决巨大的优化问题方面已经非常有效了 20 多年。然而，这些求解器的主要缺点是需要依赖于问题和复杂的参数设置才能达到良好的结果。本文提出了一种新的布谷鸟搜索算法，能够自适应其配置，特别是其种群和放弃概率。自调整过程是通过使用机器学习来管理的，其中使用聚类分析来自主和正确地计算求解过程的每个步骤所需的代理数量。目标是有效地探索可能解决方案的空间，同时减轻参数配置中的人力。我们在众所周知的集合覆盖问题上展示了有趣的实验结果，所提出的方法能够与各种最先进的算法竞争，与 20 种不同的配置相比，在一次运行中获得更好的结果。此外，将获得的结果与类似的混合仿生算法进行比较，说明该提议的有趣结果。