## 8 Final Remarks  
This work addresses the RNPP in WSNs from two MO formulations with the purpose of searching for robust methods solving this deployment problem within a realistic perspective. The first formulation includes two objectives, energy cost and average sensitivity, and the second formulation includes three objectives, the two previous ones as well as network reliability. On this basis, the authors propose to study how performs a wide range of MO metaheuristics from the three main groups in the field: evolutionary algorithms (NSGA-II, SPEA2, and MOEA/D), swarm intelligence algorithms (MO-ABC, MO-FA, and MO-GSA), and trajectory algorithms (MO-VNS and MO-VNS*).  
The eight MO metaheuristics were applied for solving four deployment scenarios, in both optimization problems, of increasing complexity, while considering a different number of RNs and communication conditions. The experimental results were analyzed through an accepted statically methodology, where two standard MO metrics were considered, i.e., hypervolume and set coverage. As a result, we concluded that MO-FA provided a robust performance independently of the number of objectives and instance size, and then MO-FA could be recommended as a general solving method for this problem. Additionally and focusing on instance size, we concluded that MO-FA provided a robust performance in small and large instances, while MO-VNS provided the best performance in medium instances.  
As future lines of research, it could be interesting to extend the network model considered. For instance, simulating additional MAC and routing protocols. Moreover, it could be interesting to try to extend the results obtained to a real WSN deployment.  
Figure 1: Average performance of the metaheuristics solving the two RNPPs.  
**Author Contributions:** [PERSON]-[PERSON] proposed the comparative study, implemented the algorithms, performed the experiments, and wrote the paper; [PERSON] proposed the experimental methodology, analysed the results, revised the mathematical notation, and collaborated in implementing the algorithms and writing the paper; [PERSON]-[PERSON] collaborated in elaborating the comparative study, adapting the algorithms to the MO case and writing the paper; [PERSON] and [PERSON] reviewed the article and suggested some of the analysis performed and the conclusions.  
**Funding:** This research has been partially funded by Ministerio de Economia y Competitividad, Spain (Projects MTM2015-63978-P, TIN2016-76259-P, and TEC2017-86722-C4-2-R), Gobierno de Extremadura, Spain (Projects GR15106 and IB16002), and European Union (European Regional Development Funds). [PERSON], [PERSON], [PERSON], and [PERSON] are supported by Proyecto CORFO Innova-Chile 14 ENII2-26905 - Nueva Ingenieria para el 2030. [PERSON] is also supported by grant CONICYT/FONDECYT/REGULAR/1171243. [PERSON] is also supported by grant CONICYT/FONDECYT/REGULAR/1160455.  
**Acknowledgments:** The authors thank two anonymous referees for comments and suggestions which have improved the content and readability of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.