Kiani, FarzadSeyyedabbasi, Amir2022-12-202022-12-202022Kiani, F., & Seyyedabbasi, A. (2023). Metaheuristic Algorithms in IoT: Optimized Edge Node Localization. In Engineering Applications of Modern Metaheuristics (pp. 19-39). Springer, Cham.1860949Xhttp://dx.doi.org/10.1007/978-3-031-16832-1_2https://hdl.handle.net/20.500.12713/3744In this study, a new hybrid method is proposed by using the advantages of Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The proposed hybrid metaheuristic algorithm tries to find the near-optimal solution with high efficiency by using the advantage of both algorithms. At the same time, the shortcomings of each will be eliminated. The proposed algorithm is used to solve the edge computing node localization problem, which is one of the important problems on the Internet of Things (IoT) systems, with the least error rate. This algorithm has shown a successful performance in solving this problem with a smooth and efficient position update mechanism. It was also applied to 30 famous benchmark functions (CEC2015 and CEC2019) to prove the accuracy and general use of the proposed method. It has been proven from the results that it is the best algorithm with a success rate of 54% and 57%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessEdge Computing NodesLocalizationMetaheuristicOptimization ProblemsInternet Of ThingsArticle2-s2.0-8514382345010.1007/978-3-031-16832-1_2N/A