Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty
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...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2017
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| Online Access: | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/51663 |
| _version_ | 1848758748612395008 |
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| author | Zhao, C. Wu, Changzhi Chai, J. Wang, X. Yang, X. Lee, J. Kim, M. |
| author_facet | Zhao, C. Wu, Changzhi Chai, J. Wang, X. Yang, X. Lee, J. Kim, M. |
| author_sort | Zhao, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:48:55Z |
| format | Journal Article |
| id | curtin-20.500.11937-51663 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:48:55Z |
| publishDate | 2017 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-516632023-02-02T03:24:11Z Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty Zhao, C. Wu, Changzhi Chai, J. Wang, X. Yang, X. Lee, J. Kim, M. 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. 2017 Journal Article http://hdl.handle.net/20.500.11937/51663 10.1016/j.asoc.2017.02.009 http://purl.org/au-research/grants/arc/LP140100873 Elsevier BV restricted |
| spellingShingle | Zhao, C. Wu, Changzhi Chai, J. Wang, X. Yang, X. Lee, J. Kim, M. Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty |
| title | Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty |
| title_full | Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty |
| title_fullStr | Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty |
| title_full_unstemmed | Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty |
| title_short | Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty |
| title_sort | decomposition-based multi-objective firefly algorithm for rfid network planning with uncertainty |
| url | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/51663 |