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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 張瑞益 | |
dc.contributor.author | Wen-Chun Chi | en |
dc.contributor.author | 紀玟君 | zh_TW |
dc.date.accessioned | 2021-06-08T01:09:23Z | - |
dc.date.copyright | 2014-08-25 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-18 | |
dc.identifier.citation | [1] R.-I. Chang, S.-Y. Lin, and Y. Hung, 'Particle swarm optimization with query-based learning for multi-objective power contract problem,' Expert Systems with Applications, vol. 39, pp. 3116-3126, 2012.
[2] J. Kennedy and R. Eberhart, 'Particle swarm optimization,' in Proceedings of IEEE international conference on neural networks, 1995, pp. 1942-1948. [3] R.-I. Chang and P.-Y. Hsiao, 'Unsupervised query-based learning of neural networks using selective-attention and self-regulation,' Neural Networks, IEEE Transactions on, vol. 8, pp. 205-217, 1997. [4] R.-I. Chang, L.-B. Lai, W.-D. Su, J.-C. Wang, and J.-S. Kouh, 'Intrusion detection by backpropagation neural networks with sample-query and attribute-query,' International Journal of Computational Intelligence Research, vol. 3, pp. 6-10, 2007. [5] A. W. McNabb, C. K. Monson, and K. D. Seppi, 'Parallel pso using mapreduce,' in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 7-14. [6] C. W. Reynolds, 'Flocks, herds and schools: A distributed behavioral model,' ACM SIGGRAPH Computer Graphics, vol. 21, pp. 25-34, 1987. [7] F. Heppner and U. Grenander, 'A stochastic nonlinear model for coordinated bird flocks,' AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, WASHINGTON, DC(USA). 1990., 1990. [8] E. O. Wilson, Sociobiology: The new synthesis: Harvard University Press, 2000. [9] X. Hu and R. Eberhart, 'Multiobjective optimization using dynamic neighborhood particle swarm optimization,' in Computational Intelligence, Proceedings of the World on Congress on, 2002, pp. 1677-1681. [10] F. Van den Bergh and A. P. Engelbrecht, 'Cooperative learning in neural networks using particle swarm optimizers,' South African Computer Journal, pp. p. 84-90, 2000. [11] L. Messerschmidt and A. P. Engelbrecht, 'Learning to play games using a PSO-based competitive learning approach,' Evolutionary Computation, IEEE Transactions on, vol. 8, pp. 280-288, 2004. [12] M. Clerc, 'Discrete particle swarm optimization, illustrated by the traveling salesman problem,' in New optimization techniques in engineering, ed: Springer, 2004, pp. 219-239. [13] R. C. Eberhart and Y. Shi, 'Comparison between genetic algorithms and particle swarm optimization,' in Evolutionary Programming VII, 1998, pp. 611-616. [14] I. C. Trelea, 'The particle swarm optimization algorithm: convergence analysis and parameter selection,' Information processing letters, vol. 85, pp. 317-325, 2003. [15] Y. Shi and R. Eberhart, 'A modified particle swarm optimizer,' in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 69-73. [16] Y. Shi and R. C. Eberhart, 'Parameter selection in particle swarm optimization,' in Evolutionary Programming VII, 1998, pp. 591-600. [17] R. C. Eberhart and Y. Shi, 'Comparing inertia weights and constriction factors in particle swarm optimization,' in Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, 2000, pp. 84-88. [18] J. Dean and S. Ghemawat, 'MapReduce: simplified data processing on large clusters,' Communications of the ACM, vol. 51, pp. 107-113, 2008. [19] A. McKenna, M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, A. Kernytsky, et al., 'The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data,' Genome research, vol. 20, pp. 1297-1303, 2010. [20] R. C. Taylor, 'An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics,' BMC bioinformatics, vol. 11, p. S1, 2010. [21] J. Dittrich and J.-A. Quiane-Ruiz, 'Efficient big data processing in Hadoop MapReduce,' Proceedings of the VLDB Endowment, vol. 5, pp. 2014-2015, 2012. [22] S. Krishnan, C. Baru, and C. Crosby, 'Evaluation of MapReduce for gridding LIDAR data,' in Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, 2010, pp. 33-40. [23] A. W. Mcnabb, C. K. Monson, and K. D. Seppi, 'MRPSO: MapReduce particle swarm optimization,' in Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007, pp. 177-177. [24] G. S. Sadasivam and D. Selvaraj, 'A novel parallel hybrid PSO-GA using MapReduce to schedule jobs in Hadoop data grids,' in Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, 2010, pp. 377-382. [25] C. Jin, C. Vecchiola, and R. Buyya, 'MRPGA: an extension of MapReduce for parallelizing genetic algorithms,' in eScience, 2008. eScience'08. IEEE Fourth International Conference on, 2008, pp. 214-221. [26] I. Aljarah and S. A. Ludwig, 'Parallel particle swarm optimization clustering algorithm based on mapreduce methodology,' in Nature and Biologically Inspired Computing (NaBIC), 2012 Fourth World Congress on, 2012, pp. 104-111. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18517 | - |
dc.description.abstract | 動機:近年隨著巨量資料(Big Data)受到關注,各種解決方案隨之而生,平行運算(parallel computing)即為其一,本論文探討以詢問式學習法改良的粒子群最佳化(particle swarm optimization, PSO)演算法如何透過Hadoop MapReduce 架構達成平行化進行運算,用以改良其不擅處理巨量資料的缺點。
作法:本研究參考以詢問式學習法改良的PSO 演算法,此改良方法加入了含糊地帶的學習法則,提高粒子搜尋到真正解答的可能性,我們試圖將此演算法於MapReduce 架構上實現,為更有效發揮其平行運算的優勢而針對學習法則稍作修改,後以二維函數的實驗結果說明修改後的方法是否依然能引導粒子跳脫局部最佳解的困境,另實測實驗架構上的機器數量對執行效率的影響。 成果:實驗結果顯示,我們針對學習法則進行修改後,搭配適當的參數設置,演算法運行結果仍優於傳統的PSO 演算法,並且所運行的資料量越大時,平行運算架構所帶來的優勢越明顯。 | zh_TW |
dc.description.provenance | Made available in DSpace on 2021-06-08T01:09:23Z (GMT). No. of bitstreams: 1 ntu-103-R01525054-1.pdf: 931895 bytes, checksum: ea9547d2adf5716f7715311d1125d76d (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 中文摘要 ii
ABSTRACT iii 第1章 緒論 1 1.1 前言 1 1.2 研究背景與動機 1 1.3 論文架構 2 第2章 文獻探討 3 2.1 基本粒子群演算法 3 2.1.1 PSO 發展背景與概念 3 2.1.2 PSO 演算法理論 4 2.2 PSO 相關研究 6 2.2.1 PSO 收斂性相關探討 6 2.2.2 QBL-PSO 7 2.3 MapReduce 10 2.3.1 MapReduce 介紹 10 2.3.2 MRPSO 11 第3章 研究架構與設計 13 3.1 研究方法概念 13 3.2 MRPSO-QBL 14 3.2.1 MRPSO-QBL 求解過程 14 3.2.2 粒子打散規則 15 3.2.3 PSO 初始化參數 19 第4章 實驗結果與討論 20 4.1 實驗與評估方式說明 20 4.1.1 實驗方式 20 4.1.2 評估方式 20 4.2 測試資料說明 21 4.2.1 Rastrigin Function 22 4.2.2 Griewank Function 23 4.2.3 Michalewicz Function 24 4.3 實驗結果 25 4.3.1 實驗環境與設定說明 25 4.3.2 第一階段實驗 – 運行時間比較 27 4.3.3 第二階段實驗 – MRPSO-QBL 參數選擇 29 4.3.4 第三階段實驗 – QBL-PSO v.s. MRPSO-QBL 31 4.3.5 延伸實驗 34 第5章 結論與未來發展 36 5.1 結論與貢獻 36 5.2 未來展望 37 5.2.1 MapReduce 配置最佳化 37 5.2.2 高維度實現 37 5.2.3 加入子演化的設計 37 5.2.4 動態設置參數 38 5.3 總結 38 REFERENCES 39 | |
dc.language.iso | zh-TW | |
dc.title | 使用MapReduce 之平行化詢問式粒子群演算法 | zh_TW |
dc.title | Parallel QBL-PSO Using MapReduce | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林正偉,張恆華,王家輝,丁肇隆 | |
dc.subject.keyword | 粒子群演算法,詢問式學習,平行運算,MapReduce, | zh_TW |
dc.subject.keyword | PSO,Query-based learning,parallel computing,MapReduce, | en |
dc.relation.page | 39 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2014-08-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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