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| Content Provider | PubMed Central |
|---|---|
| Author | Ma, Lianbo Chen, Hanning Hu, Kunyuan Zhu, Yunlong |
| Copyright Year | 2014 |
| Abstract | 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. |
| Related Links | http://dx.doi.org/10.1155/2014/941532 |
| Starting Page | 941532 |
| File Format | |
| ISSN | 23566140 |
| e-ISSN | 1537744X |
| Journal | The Scientific World Journal |
| Volume Number | 2014 |
| Language | English |
| Publisher | Hindawi Publishing Corporation |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Rights Holder | Hindawi Publishing Corporation |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Environmental Science(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine Biochemistry, Genetics and Molecular Biology Environmental Science |
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