Metaheuristics provide the means to approximately solve complex optimisation problems when exact optimisers cannot be utilised. This led to an explosion in the number of novel metaheuristics, most of them metaphor-based, using nature as a source of inspiration. Thus, keeping track of their capabilities and innovative components is an increasingly difficult task. This can be resolved by an exhaustive classification system. Trying to classify metaheuristics is common in research, but no consensus on a classification system and the necessary criteria has been established so far. Furthermore, a proposed classification system can not be deemed complete if inherently different metaheuristics are assigned to the same class by the system. In this paper we provide the basis for a new comprehensive classification system for metaheuristics. We first summarise and discuss previous classification attempts and the utilised criteria. Then we present a multi-level architecture and suitable criteria for the task of classifying metaheuristics. A classification system of this kind can solve three main problems when applied to metaheuristics: organise the huge set of existing metaheuristics, clarify the innovation in novel metaheuristics and identify metaheuristics suitable to solve specific optimisation tasks.

当无法使用精确的优化器时，元启发法提供了近似解决复杂优化问题的方法。这就导致了以自然为灵感来源的新型元启发式方法的爆炸式增长，其中大多数是基于隐喻的。因此，跟踪其功能和创新组件是一项日益艰巨的任务。这可以通过详尽的分类系统解决。尝试对元启发法进行分类在研究中很常见，但是到目前为止，在分类系统和必要的标准方面尚未达成共识。此外，如果系统固有地将不同的元启发法分配给同一类别，则不能认为该分类系统是完整的。在本文中，我们为新的元启发式综合分类系统提供了基础。我们首先总结并讨论以前的分类尝试和使用的标准。然后，我们提出了一个多层次的体系结构和适用于分类元启发法任务的标准。这种分类系统在应用于元启发式方法时可以解决三个主要问题：组织大量现有的元启发式方法，阐明新颖的元启发式方法的创新，并确定适合解决特定优化任务的元启发式方法。