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研究生:林品良
研究生(外文):Pin-Liang Lin
論文名稱:限制推理型之粒子群演算法於解決分群問題之研究
論文名稱(外文):Research of a Constraint-Based Particle Swarm Optimization Approach for Solving Clustering Problems
指導教授:徐培倫徐培倫引用關係
學位類別:碩士
校院名稱:健行科技大學
系所名稱:電子工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:54
中文關鍵詞:限制推理資料分群粒子群演算法
外文關鍵詞:Constraint-Based ReasoningData ClusteringParticle Swarm Optimization.
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本論文提出一個限制推理型之粒子群演算法用以解決資料分群問題。由於粒子群演算法主要是透過適應值函數來評估資料集中分群距離之總和,但是當問題的複雜度提高時,除了搜尋時間會相對提高外,且有可能會陷入區域最佳解之困境。基於上述之缺點,本研究根據使用者要求,加入自訂限制條件,整合限制推理及粒子過濾機制於粒子群演算法架構中,可以有效減少粒子產生的搜尋空間,使得粒子群演算法能夠更快速的找出符合限制之最佳解。根據實驗結果顯示,限制式粒子群演算法較一般傳統的粒子群演算法有更快速且較符合目標之結果。

This paper proposes a constraint-based particle swarm optimization approach to solve the problem of data clustering. Since the existing particle swarm optimization algorithm designed for searching the cluster centroids is mainly to evaluate via a fitness function. Major drawback of Particle Swarm Optimization for the problem of data clustering is computation inefficiency when the complexity of the clustering problem increases. In this paper, we propose a constraint-based particle swarm optimization approach for solving the clustering problems. This study integrated constraint-based reasoning mechanism to reduce the search space and produce better solutions. The proposed approach is compared with a regular Particle Swarm Optimization using UCI repository data sets. According to the experimental results show that the constraint-based particle swarm optimization have faster and more consistent with the user''s preferences than a regular Particle Swarm Optimization.

摘  要 i
Abstract ii
誌  謝 iii
目  錄 iv
表 目 錄 vi
圖 目 錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 2
1.4 論文架構 3
第二章 文獻探討 5
2.1 粒子群演算法 5
2.1.1 粒子群演算法簡介 5
2.1.2 粒子群演算法之特性 8
2.1.3 粒子群演算法之運算流程 9
2.1.4 粒子群演算法之參數設定 11
2.2 限制規劃 12
2.2.1 限制滿足問題 13
2.2.2 限制推理 14
2.3 資料分群與粒子群演算法 19
第三章 研究方法 21
3.1 問題分析 21
3.2 研究架構 21
3.3 研究架構分析 23
第四章 實驗結果與分析 29
4.1 資料集介紹 29
4.2 實驗設計 31
4.3 實驗結果分析 34
第五章 結論與未來研究方向 49
5.1 結論 49
5.2 未來研究方向 50
參考文獻 51
簡歷 54

1.徐培倫,林品良,「應用粒子群演算法於解決資料探勘問題之研究」,2015智慧城市與檢測技術研討會,健行科技大學,民國104年6月18日。
2.詹豐澤,「限制推理型之粒子群與基因演算法於產生分類規則之研究」,健行科技大學碩士論文,民國98年7月。
3.莊麗月,邱國鑫,楊正宏,「改良式粒子族群最佳化用於資料分群」,第十七屆資訊管理暨實務研討會,嘉南藥理科技大學,民國100年12月10日。
4.黃若蘋,「啟發式演算法於資料分群問題之比較」,大同大學碩士論文,民國96年12月。
5.李維平,王雅賢,江正文,「粒子群最佳化演算法改良之研究」,科學與工程技術期刊,大葉大學,民國96年6月。
6.陳宇成,陳煇煌,「應用擇優策略粒子群結合區域搜索演算法於資料分群問題」,第二十屆資訊管理暨實務研討會,龍華科技大學,民國103年12月13日。
7.C. Bessiere, E. C. Freuder and J. C. R&;#233;gin, “Using Constraint Metaknowledge to Reduce Arc Consistency Computation,” Artificial Intelligence, Vol.107, No.1 pp.125-148, 1999.
8.C. C. Chiu, and P. L. Hsu, “A Constriant-Based Genetic Algorithm Approach for Minig Classification Rules,” IEEE Transaction on system, and cybernetics-Part C: Applications and Reviews, Vol.35, No.2, 2005.
9.L. Davis, “Applying Adaptive Algorithms to Domains,” Proceeding of the 9th International Joint Conference on Artificial Intelligence, Los Angeles, CA, pp.162-164, 1985.
10.I. D. Falco, A. D. Cioppa and E. Tarantino.,“Facing classification problems with Particle Swarm Optimization,” Applied Soft Computing, Vol. 7, pp. 652-658, 2007.
11.A. Freitas, “A genetic algorithm for generalized rule induction,” in Advances in Soft Computing—Engineering Design and Manufacturing, R. Roy, T. Furuhashi, and P. K. Chawdhry, Eds. New York: Springer-Verlag, pp. 340–353, 1999.
12.A.Freitas, ”A survey of evolutionary algorithms for data mining and knowledge discovery,” In:Ghosh A, Tsutsui S, editors. Advances in evolutionary computation. Berlin: Springer., 2002.
13.M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, John Wiley &; Sons, New York, 2000.
14.M. Halkidi,Y. Batistakis,and M. Vazirgiannis,"On Clustering Validation Techniques," Journal of Intelligent Information Systems,Vol.17,No. 2-3,pp.107-145,2001.
15.A. Hatamlou, S. Abdullah and H Nezamabadi-pour, “Application of Gravitational Search Algorithm on Data Clustering” Rough Sets and Knowledge Technology, Vol. 6954,pp.337-346,2011.
16.R. M. Haralickand G. L. Elliot, “Increasing Tree Search Efficiency for Constraint Satisfaction Problems,” Artificial Intelligence,Vol.14, No.3,pp.263-313, 1980.
17.J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press, 1975.
18.J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” IEEE International Conference on Neural Networks, vol.4, pp.1942-1948, Dec. 1995.
19.G.Liang, et al., “Acquisition of pattern classification rule based on particle swarm optimization,” Proceedings of IEEE International Conference on neutral Networks, Perth, Australia, pp. 1942-1948,2004.
20.D.W. Van der Merwe and A.P. Engelbrecht,"Data clustering using particle swarm optimization,"Proceedings of IEEE Congress on Evolutionary Computation 2003 (CEC 2003),pp.215-220,2003.
21.K. Mackworth, “Consistency in Networks of Relations,” Artificial Intelligence, Vol.8, No.1, pp.99-118, 1977.
22.R. Mohr and T. C. Henderson, “Arc and Path Consistency Revised,” Artificial Intelligence, Vol.28, No.2, pp.225-233, 1986.
23.D.OliverSmith and J. Holland, “A Study of Permutation Crossover Operators on the Traveling Salesman Problem,” Proceedings of the 2nd International Conference on Genetic Algorithms, Cambridge, MA, USA, pp. 224-230, 1987.
24.M. Perlin, “Arc Consistency for Factorable Relations,” Artificial Intelligence, Vol.53, No.2-3, pp.329-342, 1992
25.R.J. Roiger and M.W. Geatz, Data Mining A Tutorial-based Primer, 2003.
26.S.L.Salzgerg, “On comparing Classifiers: Pitfalls to Avoid and a Recommended Approach”, Data Mining and Knowlede Discovery, Boston, Vol. 1, pp. 317-327, 1997.
27.I. B. Saida, K. Nadjet, and B. Omar,"A New Algorithm for Data Clustering Based on Cuckoo Search Optimization," Genetic and Evolutionary Computing,Vol.238,pp.55-64,2014.
28.H. S.Wang, C. W.Yeh, C. P.Huang, and W. W.Chang, “Using association rules and particle swarm optimization approach for part change,” Expert Systems with Applications, Vol. 36, pp. 8178-8184, 2009.
29.Z.Wang, X.Sun and D.Zhang, “APSO-Based Classification Rule Mining Algorithm,” Spring-Verlag BerlinHeidelberg,ICIC 2007, LNAI 4682, pp. 377-384, 2007.
30.S.Yang and D.Wang “Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling,” IEEE Transactions on Neural Networks, Vol. 11, No. 2, pp.474-486, 2000.


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