# Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm
## 2 Related Work  
Cellular manufacturing systems are widely considered in the industry due to the several benefits involved in economic terms; indeed, preliminary investigations in this context date back from 1960 with the work of [PERSON] [12] about production flow analysis. From this point, mathematical programming was strongly involved in solving the problem. Several examples in this line can be found from classic linear programming to more advanced goal programming procedures [11; 13; 14; 15; 16; 17; 18]. Techniques derived from mathematical programming and artificial intelligence, such as constraint programming, were also reported [2; 3]. More recently, the room for metaheuristics in this area has been growing since its use is more appropriate when problem instances are intractable with exact methods because of computing time restrictions. In this line, some authors applied classic metaheuristics as tabu search for solving different approaches of the problem [19; 20; 21; 22; 23]. The presence of genetic algorithms (GAs) is also large. For instance, a bi-criteria model for solving cell formation problems was presented in [24]. A similar approach was reported in [25], but involving three objective functions. Alternative routines for the part-flow were incorporated in [26; 27]. In [28], another GA was proposed but mostly oriented to minimize the inter-cell flow cost rather than minimizing the number of inter-cell movements. Industrial cases from the automobile and steel industry were presented in [29; 30] while applying GAs. Hybrid GAs, as well as variants such as the predator-prey GA, were also explored [31; 32; 33; 34]. Classicsimulated annealing, differential evolution, scatter search, and particle optimization algorithms also participate in the literature [35; 36; 37; 38]. From the previous analysis, the authors reach that modern swarm intelligence metaheuristics are the most widely applied techniques for solving the problem in recent years. For instance, the authors may cite the following works, among many others. In [39], a migrating bird algorithm was applied, which was later parallelized in [40]. The artificial fish swarm algorithm and the shuffled frog leaping algorithm were applied in [41; 42]. Bat algorithm and its autonomous version were applied in [43; 44], respectively. The firefly algorithm was considered in [45; 46], the cat swarm optimization in [47] and the flower pollination algorithm in [48]. Black hole algorithm has successfully been applied to fine tune a machine learning approach in [49]. Finally, an Egyptian culture optimization algorithm was reported in [50]. As introduced before, although metaheuristics are widely considered in the literature for solving complex optimization problems, the usage of these methods is limited by the needed of configuring parameters, which definitively affect how the search is performed. This situation implies that it is needed to solve the fitting problem of the algorithm. However, it is unlikely to get the optimal solution to the problem because of the number of possible combinations. This fact means that the performance of the algorithm could be biased due to the usage of an inadequate configuration. A solution to this problem could be to consider metaheuristics with the less possible number of parameters. However, parameters usually provide metaheuristics the capacity of providing a good solution in a great range of instances and problems, i.e., adaptability. Another possibility is the one considered in this paper, where the authors study how to provide autonomous adaptability during the solving time. This is a novel concept, which has not been extensively considered in the literature. For instance, we may cite the following works defining an autonomous strategy for guiding specific metaheuristics [51; 52; 53]. On this basis, in this work, the authors study how to provide autonomous adaptability to the water cycle algorithm, which is a metaheuristic that was successfully applied to solve several optimization problems [54; 55; 56; 57].