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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

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Mentions: Produce the initial HABC population. Initialize N species; each is divided into K subpopulation. Each subpopulation Pij possesses M individuals, where i ∈ [1,…, N], j ∈ [1,…, K]. Then N × M × K  (N ≥ 2, M ≥ 2, K ≥ 1) individuals based on D-dimensional objective should be randomly generated as shown in Figure 17, where xijks  (i ∈ [1,…, M], j ∈ [1,…, S], k ∈ [1,…, K], s ∈ [1 … N],  S = D/K) is the position of the jth state variable in the ith individual of kth subpopulation of the sth species. K is the group number by dividing D-dimensions into S-dimension. Emphasize that the group number K is dynamically changed by random grouping approach by (11).


Hierarchical artificial bee colony algorithm for RFID network planning optimization.

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

© Copyright Policy - open-access
Related In: Results  -  Collection

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

Mentions: Produce the initial HABC population. Initialize N species; each is divided into K subpopulation. Each subpopulation Pij possesses M individuals, where i ∈ [1,…, N], j ∈ [1,…, K]. Then N × M × K  (N ≥ 2, M ≥ 2, K ≥ 1) individuals based on D-dimensional objective should be randomly generated as shown in Figure 17, where xijks  (i ∈ [1,…, M], j ∈ [1,…, S], k ∈ [1,…, K], s ∈ [1 … N],  S = D/K) is the position of the jth state variable in the ith individual of kth subpopulation of the sth species. K is the group number by dividing D-dimensions into S-dimension. Emphasize that the group number K is dynamically changed by random grouping approach by (11).

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