Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning OptimizationReport as inadecuate




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The Scientific World Journal - Volume 2014 2014, Article ID 941532, 21 pages -

Research Article

Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office VII, Nanta Street No. 114, Dongling District, Shenyang 110016, China

University of Chinese Academy of Sciences, Beijing 100039, China

Received 22 October 2013; Accepted 4 December 2013; Published 23 January 2014

Academic Editors: T. Chen, Q. Cheng, and J. Yang

Copyright © 2014 Lianbo Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.





Author: Lianbo Ma, Hanning Chen, Kunyuan Hu, and Yunlong Zhu

Source: https://www.hindawi.com/



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