Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning

The RFID network planning (RNP) problem belongs to the large-scale multi-objective hard optimization problems. RNP aims to optimize the overall read region based on a set of objectives. A novel approach of hybrid firefly algorithm was developed for multi-objective RNP problem. The technique was comb...

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Main Authors: Elewe, Adel Muhsin, Hasnan, Khalid, Nawawi, Azli
Format: Article
Language:English
Published: Asian Research Publishing Network (ARPN) 2017
Subjects:
Online Access:http://eprints.uthm.edu.my/3616/
http://eprints.uthm.edu.my/3616/1/AJ%202017%20%28501%29.pdf
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author Elewe, Adel Muhsin
Hasnan, Khalid
Nawawi, Azli
author_facet Elewe, Adel Muhsin
Hasnan, Khalid
Nawawi, Azli
author_sort Elewe, Adel Muhsin
building UTHM Institutional Repository
collection Online Access
description The RFID network planning (RNP) problem belongs to the large-scale multi-objective hard optimization problems. RNP aims to optimize the overall read region based on a set of objectives. A novel approach of hybrid firefly algorithm was developed for multi-objective RNP problem. The technique was combining the Density Based Clustering method (DBSCAN) and firefly algorithm. Empirical tests were conducted on six standard RFID benchmark sets with random and clustered topologies. A comparative analysis performed with other state-of-the-art algorithms based on the same test data.Simulation results exhibited uniformly better performance in achieving maximum coverage with smaller number of deployed readers and less transmitted power.
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institution Universiti Tun Hussein Onn Malaysia
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language English
last_indexed 2025-11-15T20:04:24Z
publishDate 2017
publisher Asian Research Publishing Network (ARPN)
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spelling uthm-36162021-11-21T03:37:50Z http://eprints.uthm.edu.my/3616/ Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning Elewe, Adel Muhsin Hasnan, Khalid Nawawi, Azli TK7800-8360 Electronics The RFID network planning (RNP) problem belongs to the large-scale multi-objective hard optimization problems. RNP aims to optimize the overall read region based on a set of objectives. A novel approach of hybrid firefly algorithm was developed for multi-objective RNP problem. The technique was combining the Density Based Clustering method (DBSCAN) and firefly algorithm. Empirical tests were conducted on six standard RFID benchmark sets with random and clustered topologies. A comparative analysis performed with other state-of-the-art algorithms based on the same test data.Simulation results exhibited uniformly better performance in achieving maximum coverage with smaller number of deployed readers and less transmitted power. Asian Research Publishing Network (ARPN) 2017-02 Article PeerReviewed text en http://eprints.uthm.edu.my/3616/1/AJ%202017%20%28501%29.pdf Elewe, Adel Muhsin and Hasnan, Khalid and Nawawi, Azli (2017) Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning. ARPN Journal of Engineering and Applied Sciences, 12 (3). pp. 834-840. ISSN 1819-6608
spellingShingle TK7800-8360 Electronics
Elewe, Adel Muhsin
Hasnan, Khalid
Nawawi, Azli
Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning
title Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning
title_full Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning
title_fullStr Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning
title_full_unstemmed Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning
title_short Hybridized firefly algorithm for multi-objective radio frequency identification (RFID) network planning
title_sort hybridized firefly algorithm for multi-objective radio frequency identification (rfid) network planning
topic TK7800-8360 Electronics
url http://eprints.uthm.edu.my/3616/
http://eprints.uthm.edu.my/3616/1/AJ%202017%20%28501%29.pdf