The Unequal Area Facility Layout Problem (UA-FLP) is a relevant task in industrial manufacturing, in which the disposition of a number of facilities (or departments) in a manufacturing system must be obtained, under several optimization criteria and different constraints. The UA-FLP is a hard optimization problem, in which traditional optimization techniques do not obtain good results. Thus, it has been successfully tackled with different heuristics and meta-heuristics in the last years. In this work we address the UA-FLP with a multi-method ensemble approach, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL). It is a novel multi-method evolutionary algorithm that encourages the evolution of several searching procedures at the same time over a single population. The CRO-SL has been previously applied to very difficult optimization problems, obtaining excellent performance. In this case, we adapt the CRO-SL to the UA-FLP, by means of increasing the diversity generation within the algorithm, which is helpful to improve the exploration of the searching space, avoiding to fall into local minima. Specifically, we propose to include several reproduction mechanisms (adapted to the UA-FLP) within each substrate of the algorithm, which will highly increase the diversity generation in the CRO-SL. An exhaustive experimental study of the CRO-SL performance in a large number of UA-FLP instances is carried out, including a comparison with the state-of-the-art algorithms for this problem. We will show the ability of the CRO-SL to reach or surpass the best-known solutions in most of the tested UA-FLP cases.

不等面积的设施布局问题（UA-FLP）是工业制造中的一项相关任务，其中许多设施（或部门）的布置）必须在多个优化条件和不同约束条件下获得。UA-FLP是一个困难的优化问题，传统的优化技术无法获得良好的效果。因此，在最近几年中，已经成功地用不同的启发式方法和元启发式方法解决了它。在这项工作中，我们使用多方法集成方法，即带有基底层的珊瑚礁优化算法（CRO-SL），来解决UA-FLP。它是一种新颖的多方法进化算法，可鼓励在单个总体上同时进行多个搜索过程的进化。CRO-SL先前已应用于非常困难的优化问题，获得了出色的性能。在这种情况下，我们通过增加算法中的分集生成，使CRO-SL适应UA-FLP，这有助于改善对搜索空间的探索，避免陷入局部最小值。具体来说，我们建议在算法的每个基质内包括几种复制机制（适用于UA-FLP），这将大大增加CRO-SL中的多样性产生。对大量UA-FLP实例中的CRO-SL性能进行了详尽的实验研究，其中包括与该问题的最新算法的比较。在大多数经过测试的UA-FLP案例中，我们将展示CRO-SL达到或超过最著名解决方案的能力。这将大大增加CRO-SL中的多样性产生。对大量UA-FLP实例中的CRO-SL性能进行了详尽的实验研究，其中包括与该问题的最新算法的比较。在大多数经过测试的UA-FLP案例中，我们将展示CRO-SL达到或超过最著名解决方案的能力。这将大大增加CRO-SL中的多样性产生。对大量UA-FLP实例中的CRO-SL性能进行了详尽的实验研究，其中包括与该问题的最新算法的比较。在大多数经过测试的UA-FLP案例中，我们将展示CRO-SL达到或超过最著名解决方案的能力。