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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

Computing time of all algorithms on different RNP problems.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig16: Computing time of all algorithms on different RNP problems.

Mentions: Algorithm complexity analysis is presented briefly as follows. If we assume that the computation cost of one individual in the HABC is Cost_a, the cost of the crossover operator is Cost_c and the total computation cost of HABC for one generation is N∗K∗M∗Cost_a + N∗Cost_c. However, because the heuristic algorithms used in this paper cannot ensure comprehensive convergence, it is very difficult to give a brief analysis in terms of time for all algorithms. By directly evaluating the algorithmic time response on different objective functions, the average computing time in 30 sample runs of all algorithms is given in Figure 16. From the results in Figure 16, it is observed that the HABC takes the most computing time in all compared algorithms and the time increasing rate of it is the highest one. This can explain that the multi-population cooperative co-evolution strategy integrated by HABC enhanced the local search ability at cost of increasing the computation amount. In summary, it is concluded that compared with other algorithms, the CMOABC requires more computing time to achieve better results.


Hierarchical artificial bee colony algorithm for RFID network planning optimization.

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

Computing time of all algorithms on different RNP problems.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig16: Computing time of all algorithms on different RNP problems.
Mentions: Algorithm complexity analysis is presented briefly as follows. If we assume that the computation cost of one individual in the HABC is Cost_a, the cost of the crossover operator is Cost_c and the total computation cost of HABC for one generation is N∗K∗M∗Cost_a + N∗Cost_c. However, because the heuristic algorithms used in this paper cannot ensure comprehensive convergence, it is very difficult to give a brief analysis in terms of time for all algorithms. By directly evaluating the algorithmic time response on different objective functions, the average computing time in 30 sample runs of all algorithms is given in Figure 16. From the results in Figure 16, it is observed that the HABC takes the most computing time in all compared algorithms and the time increasing rate of it is the highest one. This can explain that the multi-population cooperative co-evolution strategy integrated by HABC enhanced the local search ability at cost of increasing the computation amount. In summary, it is concluded that compared with other algorithms, the CMOABC requires more computing time to achieve better results.

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