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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

RFID system.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: RFID system.

Mentions: An RFID system consists of four types of important components (see Figure 1): (1) RFID tags, each placed on an object and consists of a microchip and an embedded antenna containing a unique identity, which is called Electronic Product Code (EPC); (2) RFID readers, each has more than one antenna and is responsible to send and receive data to and from the tag via radio frequency waves; (3) RFID middleware, which manages readers and filters and formats the RFID raw tag data; (4) RFID database, which records RFID raw tag data that contain information such as reading time, location, and tag EPC. In this section, a mathematical optimization model for the RNP problem based on RFID middleware is proposed.


Hierarchical artificial bee colony algorithm for RFID network planning optimization.

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

RFID system.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: RFID system.
Mentions: An RFID system consists of four types of important components (see Figure 1): (1) RFID tags, each placed on an object and consists of a microchip and an embedded antenna containing a unique identity, which is called Electronic Product Code (EPC); (2) RFID readers, each has more than one antenna and is responsible to send and receive data to and from the tag via radio frequency waves; (3) RFID middleware, which manages readers and filters and formats the RFID raw tag data; (4) RFID database, which records RFID raw tag data that contain information such as reading time, location, and tag EPC. In this section, a mathematical optimization model for the RNP problem based on RFID middleware is proposed.

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