Abstract
Nature inspired algorithms provide an efficient method to resolve the problems of RFID Network Planning optimization that are not possible with the conventional methods. The gradient of the RFID Network Planning objective function was recently used to improve the precision of global optimal solutions. Therefore, this work presents a comparative study between the Gradient-Based Cuckoo Search (GBCS) and state-of the-art algorithms in a large, complex, and dynamic RNP network. The outcome of this study analyzed the algorithms performance in terms of tag coverage, required number of readers, and interference between reader propagation areas. The present method specifies the combinatorial performance of the reader’s propagation area based on the evaluation of the tag density and location by using the Gradient-Based to manage the input representation of the Cuckoo Search. The results observed high local information for the objective function, which facilitated the choice of complex RFID Network Planning parameters that enabled the algorithm to work with big data in large scale conditions.
Published Version
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