Limits...
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.

Show MeSH

Related in: MedlinePlus

Hierarchical optimization model.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3921959&req=5

fig2: Hierarchical optimization model.

Mentions: Hence, the HABC contains two levels, namely, the bottom level and top level, to balance exploring and exploiting ability. In Figure 2, in the bottom level, with the variables decomposing strategy, each subpopulation employs the canonical ABC method to search the part-dimensional optimum in parallel. That is, in each iteration, K subpopulations in the bottom level generate K best solutions, which are constructed into a complete solution species that update to the top level. In the top level, the multispecies community adopts the information exchange mechanism based on crossover operator, by which each species can learn from its neighborhoods in a specific topology. The vectors decomposing strategy and information exchange (i.e., crossover operator) can be described in detail as follows.


Hierarchical artificial bee colony algorithm for RFID network planning optimization.

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

Hierarchical optimization model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Hierarchical optimization model.
Mentions: Hence, the HABC contains two levels, namely, the bottom level and top level, to balance exploring and exploiting ability. In Figure 2, in the bottom level, with the variables decomposing strategy, each subpopulation employs the canonical ABC method to search the part-dimensional optimum in parallel. That is, in each iteration, K subpopulations in the bottom level generate K best solutions, which are constructed into a complete solution species that update to the top level. In the top level, the multispecies community adopts the information exchange mechanism based on crossover operator, by which each species can learn from its neighborhoods in a specific topology. The vectors decomposing strategy and information exchange (i.e., crossover operator) can be described in detail as follows.

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