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Hierarchical artificial bee colony algorithm for RFID network planning optimization.

Ma L, Chen H, Hu K, Zhu Y - ScientificWorldJournal (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

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Related in: MedlinePlus

The test working area tags deployment: (a) cluster deploying with 100 tags (Cd100); (c) randomly deploying with 500 tags (Rd500).
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fig8: The test working area tags deployment: (a) cluster deploying with 100 tags (Cd100); (c) randomly deploying with 500 tags (Rd500).

Mentions: The readers used here are mobile and the tags are passive. According to the references [6, 33–37], the related RFID readers' parameters can be set as in Table 6. Here the interrogation range according to the reader radiated power is computed as in [38]. The proposed algorithm is evaluated against two different RNP instances, namely, Cd100 and Rd500. The instance of Cd100 is tested on a 30 m × 30 m working space with 100 clustered distributed tags. Another instance, namely, Rd500, contains 500 randomly distributed tags in a 150 m × 150 m working space (shown in Figure 8). In this experiment, the parameters setting for HABC, ABC, PSO, EGA, and CCEA can be the same as in Section 4.1. Especially, the PS2O algorithm proposed by us in [6] as an effective approach for solving RNP, is employed to compare with the proposed approach using HABC. For PS2O, the number of swarms and swarm size can be set by n = 10, m = 5. The constriction factor is used with χ = 0.729, and then the learning rates c1 = c2 = c3 = 1.3667 [6].


Hierarchical artificial bee colony algorithm for RFID network planning optimization.

Ma L, Chen H, Hu K, Zhu Y - ScientificWorldJournal (2014)

The test working area tags deployment: (a) cluster deploying with 100 tags (Cd100); (c) randomly deploying with 500 tags (Rd500).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3921959&req=5

fig8: The test working area tags deployment: (a) cluster deploying with 100 tags (Cd100); (c) randomly deploying with 500 tags (Rd500).
Mentions: The readers used here are mobile and the tags are passive. According to the references [6, 33–37], the related RFID readers' parameters can be set as in Table 6. Here the interrogation range according to the reader radiated power is computed as in [38]. The proposed algorithm is evaluated against two different RNP instances, namely, Cd100 and Rd500. The instance of Cd100 is tested on a 30 m × 30 m working space with 100 clustered distributed tags. Another instance, namely, Rd500, contains 500 randomly distributed tags in a 150 m × 150 m working space (shown in Figure 8). In this experiment, the parameters setting for HABC, ABC, PSO, EGA, and CCEA can be the same as in Section 4.1. Especially, the PS2O algorithm proposed by us in [6] as an effective approach for solving RNP, is employed to compare with the proposed approach using HABC. For PS2O, the number of swarms and swarm size can be set by n = 10, m = 5. The constriction factor is used with χ = 0.729, and then the learning rates c1 = c2 = c3 = 1.3667 [6].

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

Show MeSH
Related in: MedlinePlus