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北京石油化工学院2026年研究生招生接收调剂公告
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hylz1008: 金币+5 2014-12-17 23:24:37
oven1986: LS-EPI+1, 感谢应助。 2014-12-18 18:29:50
number:


20132516429338






Title:

Hill-climbing and pattern ant colony hybrid Bayesian optimization algorithm






Authors:

Hu, Yun'an1 ; Liu, Zhen1; Song, Ruihua2; Shi, Jianguo3





Author affiliation:

1Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, Shandong, China






2Department of Training, Naval Aeronautical and Astronautical University, Yantai 264001, Shandong, China






3Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, Shandong, China






Corresponding author:

Hu, Y. (hya507@sina.com)






Source title:

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)






Abbreviated source title:

Huazhong Ligong Daxue Xuebao






Volume:

41






Issue:

5






Issue date:

May 2013






Publication year:

2013






Pages:

90-95






Language:

Chinese






ISSN:

16714512






Document type:

Journal article (JA)






Publisher:

Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China






Abstract:

To overcome the low evolution efficiency and long convergence time in the traditional Bayesian optimization algorithm (BOA), a new hybrid BOA was proposed. The fitness inherence and individual local search were incorporated in the new algorithm to enhance the evolution efficiency. In order to promote the construction speed of Bayesian network structure, hill-climbing and pattern ant colony was used to learn the structure of Bayesian network. The convergence of new hybrid BOA was also analyzed in the paper. The simulation results of benchmark functions show that the new hybrid algorithm performs well than before in promoting the precision and reducing the time complexity. The new algorithm was used to target allocation problem. The validity and superiority of the algorithm were also proved by the simulation results.






Number of references:

14






Main heading:

Algorithms






Controlled terms:

Bayesian networks






Uncontrolled terms:

Ant colony algorithms  -  Bayesian optimization algorithms  -  Hill-climbing methods  -  Local search  -  Pattern  -  Target allocations






Classification code:

723 Computer Software, Data Handling and Applications -  921 Mathematics -  921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory






Database:

Compendex






Compilation and indexing terms, © 2014 Elsevier Inc.



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