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论文题目：Hill—clmbing and pattern ant colony hybrid Bayesian optimization algorithm 名称：爬山法与模式蚁群算法混合的贝叶斯优化算法 作者：Hu yunan , Liu zhen 期刊： 华中科技大学学报（自然科学版），2013年，第41卷第9期 Journal of Huazhong University of Science and Technology (Natural Science Edition) |
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baiyuefei
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3楼2014-12-17 08:13:28
baiyuefei
版主 (文学泰斗)
风雪
- LS-EPI: 1647
- 应助: 4642 (副教授)
- 贵宾: 46.969
- 金币: 658102
- 散金: 11616
- 红花: 995
- 沙发: 81
- 帖子: 69424
- 在线: 13328.1小时
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【答案】应助回帖
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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
感谢参与，应助指数 +1
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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. Full-text and Local Holdings Links |
2楼2014-12-17 08:13:08
