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dc.contributor.authorZhao, C.
dc.contributor.authorWu, Changzhi
dc.contributor.authorChai, J.
dc.contributor.authorWang, X.
dc.contributor.authorYang, X.
dc.contributor.authorLee, J.
dc.contributor.authorKim, M.
dc.identifier.citationZhao, C. and Wu, C. and Chai, J. and Wang, X. and Yang, X. and Lee, J. and Kim, M. 2017. Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Applied Soft Computing. 55: pp. 549-564.

Radio frequency identification (RFID) is widely used for item identification and tracking. Due to the limited communication range between readers and tags, how to configure a RFID system in a large area is important but challenging. To configure a RFID system, most existing results are based on cost minimization through using 0/1 identification model. In practice, the system is interfered by environment and probabilistic model would be more reliable. To make sure the quality of the system, more objectives, such as interference and coverage, should be considered in addition to cost. In this paper, we propose a probabilistic-based multi-objective optimization model to address these challenges. The objectives to be optimized include number of readers, interference level and coverage of tags. A decomposition-based firefly algorithm is designed to solve this multi-objective optimization problem. Virtual force is integrated into random walk to guide readers moving in order to enhance exploitation. Numerical simulations are introduced to demonstrate and validate our proposed method. Comparing with existing methods, such as Non-dominated Sorting Genetic Algorithm-II and Multi-objective Particle Swarm Optimization approaches, our proposed method can achieve better performance in terms of quality metric and generational distance under the same computational environment. However, the spacing metric of the proposed method is slightly inferior to those compared methods.

dc.publisherElsevier BV
dc.titleDecomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty
dc.typeJournal Article
dcterms.source.titleApplied Soft Computing
curtin.departmentDepartment of Construction Management
curtin.accessStatusFulltext not available

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