Metaheuristic algorithms have become one of the preferred approaches for solving optimization problems. Finding the best metaheuristic for a given problem is often difficult due to the large number of available approaches and possible algorithmic designs. Moreover, high-performing metaheuristics often combine general-purpose and problem-specific algorithmic components. We propose here an approach for automatically designing metaheuristics using a flexible framework of algorithmic components, from which algorithms are instantiated and evaluated by an automatic configuration method. The rules for composing algorithmic components are defined implicitly by the properties of each algorithmic component, in contrast to previous proposals, which require a handwritten algorithmic template or grammar. As a result, extending our framework with additional components, even problem-specific or user-defined ones, automatically updates the design space. Furthermore, since the generated algorithms are made up of components, they can be easily interpreted. We provide an implementation of our proposal and demonstrate its benefits by outperforming previous research in three distinct problems from completely different families: a facility layout problem, a vehicle routing problem and a clustering problem.

中文翻译：

---

组合优化问题元启发式算法的自动生成


元启发式算法已成为解决优化问题的首选方法之一。由于存在大量可用方法和可能的算法设计，为给定问题找到最佳元启发式方法通常很困难。此外，高性能元启发法通常结合通用和特定问题的算法组件。我们在这里提出了一种使用灵活的算法组件框架自动设计元启发式的方法，其中算法通过自动配置方法进行实例化和评估。组成算法组件的规则是由每个算法组件的属性隐式定义的，这与之前的提案不同，之前的提案需要手写的算法模板或语法。因此，使用附加组件扩展我们的框架，甚至是针对特定问题或用户定义的组件，都会自动更新设计空间。此外，由于生成的算法是由组件组成的，因此它们可以很容易地解释。我们提供了我们提案的实现，并通过在来自完全不同系列的三个不同问题上超越之前的研究来证明其优点：设施布局问题、车辆路径问题和聚类问题。