請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66048完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王偉仲(Wei-Chung Wang) | |
| dc.contributor.author | Ming-Sian Wu | en |
| dc.contributor.author | 吳明賢 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:20:08Z | - |
| dc.date.available | 2014-06-29 | |
| dc.date.copyright | 2012-06-29 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-06-25 | |
| dc.identifier.citation | [1] Berger, M. P. F., King, J., and Wong, W. K. (2000). Minimax D-optimal Designs for Item Response Theory Models. Psychometrika. 65(3), 377-390.
[2] Chai-ead, N., Aungkulanon, P., Luangpaiboon, P. (2011). Bees and firefly algorithms for noisy non-linear optimisation problems. Prof. Int. Multiconference of Engineers and Computer Scientists 2011 2, 1449-1454. [3] Dette, H. and Sahm, M. (1998). Minimax Optimal Designs in Nonlinear Regression Models. Statistica Sinica. 8, 1249-1264. [4] Dette, H. and Wong, W. K. (1999). E-optimal Designs for the Michaelis-Menten Model. Statistics and Probability Letters. 44(4), 405-408. [5] Eberhart, R. and Kennedy, J. (1995). A New Optimizer Using Particle Swarm Theory. Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on. pages 39-43. IEEE. [6] Fedorov, V. V. (1980). Convex design theory. Mathematische Operationsforschung und Statistik 11, 403-413. [7] Geem, Z. W., Kim, J. H. and Loganathan, G. V. (2001). A new heuristic optimization: Harmony search. Simulation. 76(2), 60-68. [8] Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 0262082136. [9] Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Turkey. [10] King, J. and Wong, W. K. (2000). Minimax D-optimal Designs for the Logistic Model. Biometrics. 56(4), 1263-1267. [11] Kirkpatrick, S., Gelatt, C. D. and Vecchi, M. P. (1983). Optimization by simulated annealing. Science. 220 (4598), 671-680. [12] Murty, V. N. (1971). Minimax Designs. Journal of the American Statistical Association. 66, 319-320. [13] Yang, X. S. (2008) Nature-Inspired Metaheuristic Algorithms, Luniver Press, UK. [14] Yang, X. S. and Deb, S. (2009). Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing (NaBIC 2009). IEEE Publication, USA. , 210-214. [15] Yang, X. S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). SCI 284, 65-74. [16] Yang, X. S. (2010). Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons. [17] Wong, W. K. and Cook, R. D. (1993). Heteroscedastic G-optimal Designs. Journal of the Royal Statistical Society, Ser. B. 55(4), 871-880 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66048 | - |
| dc.description.abstract | 啟發式演算法被廣泛應用於解決許多最佳實驗設計問題。在本文中, 我們運用啟發式演算法求解四種最佳化試驗設計問題的例子。首先, 我們提出並說明這四個例子, 並簡述所使用於比較的演算法, 如蝙蝠演算法, 杜鵑鳥搜索演算法, 基因演算法, 模擬退火演算法, 人工蜂群演算法, 螢火蟲演算法, 音諧搜索演算法和粒子群優化演算法。介紹完後, 我們提出四種例子的比較數值結果並進一步作討論。最後, 從數值結果顯示與其他演算法相比較下, 杜鵑鳥搜索演算法和粒子群優化演算法有最佳的性能。這些數值研究僅針對此篇文章所提出的例
子, 可能並不適用於其他的例子。 | zh_TW |
| dc.description.abstract | Metaheuristic algorithms are widely used in solving many optimal experimental design problems. In this paper, we demonstrate the metaheuristic algorithms to construct four optimal experimental designs. First, we proposed the four examples for optimization design problems and presented the outlines of algorithm for comparison such as bat-inspired algorithm, cuckoo search, genetic algorithm, simulated annealing, artificial bee colony algorithm, firefly algorithm, harmony search and particle swarm optimization. After stated the algorithms, the numerical results of the comparison for the four examples are presented and discussed further. Finally, the conclusion suggested that cuckoo search and particle swarm optimization have the best performance in contrast with the other algorithms from the numerical results. The conclusions are drawn from the specific numerical studies and may not apply to other examples. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:20:08Z (GMT). No. of bitstreams: 1 ntu-101-R95221013-1.pdf: 877236 bytes, checksum: aa715290da824750ab244f57ac83ccf0 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . . . . .i
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ii 英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . iii 1 Introduction 1 2 Examples for Optimal Experimental Designs 2.1 Example 1: E-optimal designs for the Michaelis-Menten model . . . . . . . . . . 4 2.2 Example 2: Locally minimax single parameters optimal designs for the Double Exponential model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Example 3: A D-optimal design for the two-parameter logistic regression model 5 2.4 Example 4: A heteroscedastic minimax design for a polynomial model on the prototype design interval X = [−1, 1] with various efficiency functions . . . . . . 6 3 Algorithms 3.1 Bat-Inspired Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Cuckoo Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.3 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.5 Artificial Bee Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.6 Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.7 Harmony Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.8 Parcicle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Numerical Results and Discussions 4.1 Example 1: E-optimal designs for the Michaelis-Menten model . . . . . . . . . . 18 4.2 Example 2: Locally minimax single parameters optimal designs for the Double Exponential model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.1 Hypothesis of symmetric for Double Exponential model . . . . . . . . . . 23 4.2.2 Hypothesis of non-symmetric for Double Exponential model . . . . . . . 27 4.3 Example 3: A D-optimal design for the two-parameter logistic regression model. 30 4.3.1 A locally D-optimal design for the two-parameter logistic regression model when we have single point for the two parameters. . . . . . . . . . . . . . 30 4.3.2 A minimax D-optimal design for the two-parameter logistic regression model when we have plausible ranges for the two parameters. . . . . . . 34 4.4 Example 4: A heteroscedastic minimax design for a polynomial model on the prototype design interval X = [−1, 1] with various efficiency functions. . . . . . 40 5 Conclusions 45 | |
| dc.language.iso | zh-TW | |
| dc.subject | 螢火蟲演算法 | zh_TW |
| dc.subject | 杜鵑鳥搜索演算法 | zh_TW |
| dc.subject | 音諧搜索演算法 | zh_TW |
| dc.subject | 粒子群優化演算法 | zh_TW |
| dc.subject | 啟發式演算法 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 蝙蝠演算法 | zh_TW |
| dc.subject | 模擬退火演算法 | zh_TW |
| dc.subject | 人工蜂群演算法 | zh_TW |
| dc.subject | harmony search | en |
| dc.subject | particle swarm optimization | en |
| dc.subject | firefly algorithm | en |
| dc.subject | artificial bee colony algorithm | en |
| dc.subject | simulated annealing | en |
| dc.subject | genetic algorithm | en |
| dc.subject | cuckoo search | en |
| dc.subject | bat-inspired algorithm | en |
| dc.subject | Metaheuristic algorithm | en |
| dc.title | 使用啟發式演算法求解最佳化實驗設計 | zh_TW |
| dc.title | Constructing Optimal Experimental Designs by Meta-heuristic Algorithms | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳瑞彬(Ray-Bing Chen),蔡碧紋(Pi-Wen Tsai) | |
| dc.subject.keyword | 啟發式演算法,蝙蝠演算法,杜鵑鳥搜索演算法,基因演算法,模擬退火演算法,人工蜂群演算法,螢火蟲演算法,音諧搜索演算法,粒子群優化演算法, | zh_TW |
| dc.subject.keyword | Metaheuristic algorithm,bat-inspired algorithm,cuckoo search,genetic algorithm,simulated annealing,artificial bee colony algorithm,firefly algorithm,harmony search,particle swarm optimization, | en |
| dc.relation.page | 46 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-06-25 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 數學研究所 | zh_TW |
| 顯示於系所單位： | 數學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-101-1.pdf 未授權公開取用 | 856.68 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。