Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations fr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/941532 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849308705386921984 |
|---|---|
| author | Lianbo Ma Hanning Chen Kunyuan Hu Yunlong Zhu |
| author_facet | Lianbo Ma Hanning Chen Kunyuan Hu Yunlong Zhu |
| author_sort | Lianbo Ma |
| collection | DOAJ |
| description | This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. |
| format | Article |
| id | doaj-art-0d7a7e47b3d74f8ab3404b2ab5de9517 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-0d7a7e47b3d74f8ab3404b2ab5de95172025-08-20T03:54:24ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/941532941532Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning OptimizationLianbo Ma0Hanning Chen1Kunyuan Hu2Yunlong Zhu3Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office VII, Nanta Street No. 114, Dongling District, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office VII, Nanta Street No. 114, Dongling District, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office VII, Nanta Street No. 114, Dongling District, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office VII, Nanta Street No. 114, Dongling District, Shenyang 110016, ChinaThis paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.http://dx.doi.org/10.1155/2014/941532 |
| spellingShingle | Lianbo Ma Hanning Chen Kunyuan Hu Yunlong Zhu Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization The Scientific World Journal |
| title | Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization |
| title_full | Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization |
| title_fullStr | Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization |
| title_full_unstemmed | Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization |
| title_short | Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization |
| title_sort | hierarchical artificial bee colony algorithm for rfid network planning optimization |
| url | http://dx.doi.org/10.1155/2014/941532 |
| work_keys_str_mv | AT lianboma hierarchicalartificialbeecolonyalgorithmforrfidnetworkplanningoptimization AT hanningchen hierarchicalartificialbeecolonyalgorithmforrfidnetworkplanningoptimization AT kunyuanhu hierarchicalartificialbeecolonyalgorithmforrfidnetworkplanningoptimization AT yunlongzhu hierarchicalartificialbeecolonyalgorithmforrfidnetworkplanningoptimization |