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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊烽正(Feng-Cheng Yang) | |
| dc.contributor.author | Szu-Ting Chou | en |
| dc.contributor.author | 周思婷 | zh_TW |
| dc.date.accessioned | 2021-05-17T09:16:20Z | - |
| dc.date.available | 2015-08-15 | |
| dc.date.available | 2021-05-17T09:16:20Z | - |
| dc.date.copyright | 2012-08-15 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-03 | |
| dc.identifier.citation | Altiparmak, F., Gen, M., Lin, L. & Paksoy, T., 2006. A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51 (1), 196-215.
Chen, D.-B., Zou, F. & Wang, J.-T., 2011. A multi-objective endocrine pso algorithm and application. Applied Soft Computing, 11 (8), 4508-4520. Chung, L.-C., 2004. A Genetic Algorithm and Objective Randomly Switched Strategy Based Multi-Objective Programming Mtehod. Thesis(MS). National Taiwan University. Deb, K., 1999. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evol. Comput., 7 (3), 205-230. Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: Nsga-ii. Evolutionary Computation, IEEE Transactions on, 6 (2), 182-197. Fischer, M., Jahn, H. & Teich, T., 2004. Optimizing the selection of partners in production networks. Robotics and Computer-Integrated Manufacturing, 20 (6), 593-601. Hajela, P. & Lin, C.Y., 1992. Genetic search strategies in multicriterion optimal design. Structural and Multidisciplinary Optimization, 4 (2), 99-107. Huband, S., Hingston, P., While, L. & Barone, L., Year. An evolution strategy with probabilistic mutation for multi-objective optimisationed.^eds. Evolutionary Computation, 2003. CEC '03. The 2003 Congress on, 2284-2291 Vol.4. Ip, W.H., Huang, M., Yung, K.L. & Wang, D., 2003. Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise. Computers & Operations Research, 30 (2), 213-231. Knowles, J. & Corne, D., Year. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisationed.^eds. Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 105 Vol. 1. Large, J.W., Jones, D.F. & Tamiz, M., 2007. Hyper-spherical inversion transformations in multi-objective evolutionary optimization. European Journal of Operational Research, 177 (3), 1678-1702. Li, X., 2003. A non-dominated sorting particle swarm optimizer for multiobjective optimization. Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI. Chicago, IL, USA: Springer-Verlag, 37-48. Murata, T., Ishibuchi, H. & Tanaka, H., 1996. Multi-objective genetic algorithm and its applications to flowshop scheduling. Computers & Industrial Engineering, 30 (4), 957-968. Pan, C.-C, 2008. Water Flow-like Algorithm for Sequencing Problems. Thesis(MS). National Taiwan University. Parsopoulos, K.E. & Vrahatis, M.N., 2002. Recent approaches to global optimization problems through particle swarm optimization. 1 (2-3), 235-306. Shih-Yuan, C., Tsung-Ying, S., Sheng-Ta, H. & Cheng-Wei, L., Year. Cross-searching strategy for multi-objective particle swarm optimizationed.^eds. Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 3135-3141. Srinivas, N. & Deb, K., 1994. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput., 2 (3), 221-248. Tripathi, P.K., Bandyopadhyay, S. & Pal, S.K., Year. Adaptive mufti-objective particle swarm optimization algorithmed.^eds. Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2281-2288. Veldhuizen, D.a.V., 1999. Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Air Force Institute of Technology. Wang, P.-Y., 2006. Water Flow-like Algorithm for Discrete Optimum Problems. Thesis(MS). National Taiwan University. Watanabe, M., Ida, K. & Gen, M., 2005. A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem. Computers & Industrial Engineering, 48 (4), 743-752. Yang, F.-C. & Wang, Y.-P., 2007. Water flow-like algorithm for object grouping problems. Journal of the Chinese Institute of Industrial Engineers, 24 (6), 475-488. Yeh, W.-C. & Chuang, M.-C., 2011. Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Systems with Applications, 38 (4), 4244-4253. Youyuan, W. & Weiping, X., Year. Research on partners selection of collaborative design based on ant and genetic algorithmed.^eds. International Conference on Natural Computation, 554-558. Zitzler, E., Deb, K. & Thiele, L., 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput., 8 (2), 173-195. Zitzler, E. & Thiele, L., 1999. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. Evolutionary Computation, IEEE Transactions on, 3 (4), 257-271. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6688 | - |
| dc.description.abstract | The newly developed optimization algorithm, Water Flow-like Algorithm, that is WFA, simulates a solution searching agent as a water flow traversing the lowest point of a terrain. The number of water flows is dynamically changed while water flows split into subflows against rough terrain and merge several flows into one single flow. Flow splitting and merging are mimicked by the WAF to conduct efficient optimum search in the solution space. In addition, water evaporation and precipitation are simulated in WFA to jump out of local optima or to broaden the searching area. This paper presents a WFA for Multi-objective Continuous Optimization Problems, namely WFA4MC. This paper presents three merging methods for different merging conditions. First, the location-based merging approach is frequently adopted in general optimization problems, either continuous or discrete ones. In addition to the location-based approach, we propose an objective-based merging approach for our multi-objective optimization problems, where a set of non-dominated solutions with objective values dispersedly distributed in the objective space is preferred.
In order to prove WFA4MC performances precisely, this research proposes Correctness and Coverness to measure non-dominated solutions in ZDT functions. Besides, the Generational Distance is used in the comparison with other heuristic Algorithms. The result showed that based on the same limit of the number of objective function calls, the WFA4MC outperform than others. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-17T09:16:20Z (GMT). No. of bitstreams: 1 ntu-101-R99546010-1.pdf: 1773769 bytes, checksum: 22e694231fb1be1d2555730ffa909670 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 iv 表目錄 vi 圖目錄 vii 第一章 導論 1 1.1研究背景 1 1.2研究目的 2 1.3研究方法 2 1.4 章節概論 3 第二章 文獻探討 4 2.1仿水流優化演算法 4 2.2多目標優化演算法 6 2.2.1 基因演算法 7 2.2.2 多目標基因演算法 7 2.2.3 粒子群演算法 9 2.2.4 多目標粒子群演算法 9 2.3文獻小結 10 第三章 多目標連續型仿水流優化演算法 11 3.1多目標連續優化問題 11 3.2 WFA4MC演算流程 12 3.3 WFA4MC整體演算程序 26 3.4多目標連續型仿水流優化演算法小結 27 第四章 多目標連續型仿水流優化演算法範例驗證 28 4.1測試的多目標優化標竿問題 28 4.2評量指標 34 4.2.1收斂距離(Generational Distance)和均勻變異(Spacing metric) 34 4.2.2正確率(Correctness)和涵蓋度(Coverness) 36 4.3多目標連續型仿水流優化演算法求解系統 39 4.4標竿問題效能測試 41 4.4.1匯流模式的比較 42 4.4.2 WFA4MC成效結果分析 45 4.5範例驗證小結 47 第五章 結論與建議 49 5.1結論 49 5.2未來研究與建議 50 參考文獻 51 附錄A 53 附錄B 67 | |
| dc.language.iso | zh-TW | |
| dc.title | 多目標連續型仿水流優化演算法 | zh_TW |
| dc.title | Water Flow-like Optimization Algorithm for Multi-objective Continuous Optimization Problems | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃奎隆(Kwei-Long Huang),羅士哲(Shih-Che Lo) | |
| dc.subject.keyword | 仿水流優化演算法,多目標連續優化問題,ZDT標竿問題,正確率(Correctness),涵蓋度(Coverness), | zh_TW |
| dc.subject.keyword | Water Flow-like Algorithm,Multi-objective Continuous Optimization Problem,ZDT functions,Correctness,Coverness, | en |
| dc.relation.page | 133 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2012-08-06 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
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