Abstract
This paper introduces genetically inspired artificial bee colony algorithm adapted for solving multiobjective radio frequency identification (RFID) network planning problem, which is a well-known hard optimization problem. Artificial bee colony swarm intelligence metaheuristic was successfully applied to a wide range of similar problems. In our proposed implementation, we incorporated genetic operators into the basic artificial bee colony algorithm to enhance the intensification process in the late iterations. Such improved version was previously tested and proved to be better than the basic variant of the artificial bee colony algorithm. In the practical experiments, we tested our proposed approach on six benchmark instances used in the literature, with clustered and random tag sets. In comparative analysis with other state-of-the-art approaches our proposed algorithm exhibited superior performance and potential for further improvements.
Milan Tuba–This research is supported by Ministry of Education, Science and Technogical Development of Republic of Srbia, Grant No. III-44006
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Tuba, M., Bacanin, N., Beko, M. (2015). Multiobjective RFID Network Planning by Artificial Bee Colony Algorithm with Genetic Operators. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_27
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DOI: https://doi.org/10.1007/978-3-319-20466-6_27
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