Moreover, in recent years, qualitative aspects have been taken into account too [9; 10; 11] with the aim of incorporating expert human knowledge into the design [12]. These qualitative aspects can be related to the relative position between facilities, aesthetic aspects, or personal subjective preferences of the designers, based on their personal background and experience, and can be previously known or simply appear during the algorithm evolution process, or they can even change during the execution time [13]. In the same way, more recently, the intervention of several human designers has been introduced in some research fields. For example, [PERSON]. [14; 15] used the combined intervention of several evaluators who judged two solutions and chose the best of them in a sort of tournament competition, obtaining a good representation of evaluators' opinions in the algorithms' results. Similarly, [PERSON]. [16; 17] used a distributed interactive genetic algorithm to incorporate the feelings of several users into a musical melody. Each user evolved a population of the algorithm and, after a certain number of generations, an exchange of solutions was done, with the result that the final melody satisfied all users' preferences. In these kinds of designs, it is necessary to reflect on the intervention of multiple designers or evaluators. In the first one, a voting system must be implemented in order to collect users' opinions and to obtain a compromise evaluation of each solution, in such a way that only the best one is selected. In this case, the simultaneous presence of all the evaluators is needed, even when the evaluation is done via the web. In the second one, a model similar to an island algorithm [18] is put into practice. In this model, the concurrent presence of each of the evaluators is needed too. Concerning the UA-FLP problem, many approaches have been used in order to solve it. Initially, some exact methods were devised and carried out, but the NP-hard character of the problem [19] makes it unapproachable for large problems in terms of computational cost. Nevertheless, [PERSON]. [20] proposed an integer programming method, limited to problems of up to five departments, and [21] improved the proposal to solve instances of up to eight departments. By their part, [PERSON]. [22; 23] presented a mixed integer programming formulation limited to problems with up to fourteen departments, and, more recently, [PERSON]. [24] found optimal solutions for problems with up to twelve departments. Given the aforementioned issues, heuristic and meta-heuristic approaches have arisen. Among them, [PERSON]. [25] suggested a multiobjective genetic algorithm combining material handling costs, adjacency, and distance between departments; meanwhile, ref. [26; 27] adopted Ant Colony Optimization Algorithms, obtaining good results on well-known problem sets. In the task of improving the algorithm's efficiency, island optimization algorithms have been implemented too [18; 28]. These kinds of algorithms have in common an improvement of the exploration of the searching space by means of maintaining the diversity of the individuals in the population, which allows the achievement of good results within a reasonable computational time. Other remarkable approaches to the problem have been Simulated Annealing [29; 30; 31; 32], Tabu Search [33; 34; 35], Wang-Landau sampling algorithms [36], and Particle Swarm Optimization [37; 38; 39], among others. More recently, Coral Reef Optimization (CRO) algorithms have been used to improve the solutions obtained. These kinds of algorithms can be classified as bio-inspired evolutionary algorithms and they simulate the processes occurring in natural coral reefs, such as reproduction, fighting for space, and depredation, which results in a sort of combination of Simulating Annealing and a Hybrid Evolutionary Algorithm [40].