The template design problem (TDP) is a hard combinatorial problem with a high number of symmetries which makes solving it more complicated. A number of techniques have been proposed in the literature to optimise its resolution, ranging from complete methods to stochastic ones. However, although metaheuristics are considered efficient methods that can find enough-quality solutions at a reasonable computational cost, these techniques have not proven to be truly efficient enough to deal with this problem. This paper explores and analyses a wide range of metaheuristics to tackle the problem with the aim of assessing their suitability for finding template designs. We tackle the problem using a wide set of metaheuristics whose implementation is guided by a number of issues such as problem formulation, solution encoding, the symmetrical nature of the problem, and distinct forms of hybridisation. For the TDP, we also propose a slot-based alternative problem formulation (distinct to other slot-based proposals), which represents another option other than the classical variation-based formulation of the problem. An empirical analysis, assessing the performance of all the metaheuristics (i.e., basic, integrative and collaborative algorithms working on different search spaces and with/without symmetry breaking) shows that some of our proposals can be considered the state-of-the-art when they are applied to specific problem instances.

模板设计问题（TDP）是具有大量对称性的硬组合问题，这使得解决它变得更加复杂。从完整方法到随机方法，文献中已经提出了许多技术来优化其分辨率。但是，尽管元启发法被认为是可以以合理的计算成本找到足够质量的解决方案的有效方法，但是这些技术尚未被证明足够有效地解决了这个问题。本文探索并分析了多种元启发式方法来解决该问题，目的是评估它们在查找模板设计中的适用性。我们使用多种元启发式方法来解决该问题，这些元启发式方法的实现受到许多问题的指导，例如问题表述，解决方案编码，问题的对称性质以及不同形式的杂交。对于TDP，我们还提出了一个基于时间段的替代问题表述（与其他基于时间段的提议不同），它代表了该问题的另一种选择，而不是基于经典变体的问题表述。一项经验分析评估了所有元启发式算法（即在不同搜索空间上工作且具有/不具有对称性破缺的基本，集成和协作算法）的性能，表明我们的某些建议在以下情况下可以被视为最新建议：它们适用于特定的问题实例。它代表了除基于问题的经典变式形式之外的另一种选择。一项经验分析评估了所有元启发式算法（即在不同搜索空间上工作且具有/不具有对称性破缺的基本，集成和协作算法）的性能，表明我们的某些建议在以下情况下可以被视为最新建议：它们适用于特定的问题实例。它代表了除了基于问题的经典变体形式之外的另一种选择。一项经验分析评估了所有元启发式方法（即在不同搜索空间上工作且有/没有对称性破缺的基本，集成和协作算法）的性能，表明我们的一些建议在以下情况下可以被视为最新建议：它们适用于特定的问题实例。