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研究生： Annisa Uswatun Khasanah
Annisa - Uswatun Khasanah
論文名稱： 整合成長式自組織映射網路與粒子群最佳化演算法於群組技術之研究
Integration of Growing Self-Organizing Map and Particle Swarm Optimization Algorithm for Group Technology
指導教授： 郭人介
Ren-Jieh Kuo
口試委員: Chao-Lung Yang
Chao-Lung Yang
Chieh-Yuan Tsai
Chieh-Yuan Tsai
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 81
中文關鍵詞： Cluster analysis 、GSOM 、PSO 、Group Technology
外文關鍵詞： Cluster analysis, GSOM, PSO, Group Technology
相關次數： 點閱：294  下載：4 
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    • This study attempts to employ Growing Self-Organizing Map (GSOM) algorithm and Particle Swarm Optimization (PSO)-based Self Organizing Map (PSOSOM) to improve the performance of SOM. The proposed GSOM+PSOSOM approach for SOM is consisted of two stages. In the first stage, GSOM is used to determine the SOM topology and then followed by PSOSOM in the second stage to fine tune the SOM weights. The proposed PSOSOM algorithm is compared with other two algorithms and also compare to CGASOM from the previous study using four benchmark datasets, Iris, Wine, Vowel, and Glass. The simulation results indicate that PSOSOM algorithm is able to find the better solution. Furthermore, the proposed approach has been also employed to Group Technology to cluster components into part families for a medical furniture manufacturer in Indonesia.
    Keywords: Cluster analysis, GSOM, PSO, Group Technology

    This study attempts to employ Growing Self-Organizing Map (GSOM) algorithm and Particle Swarm Optimization (PSO)-based Self Organizing Map (PSOSOM) to improve the performance of SOM. The proposed GSOM+PSOSOM approach for SOM is consisted of two stages. In the first stage, GSOM is used to determine the SOM topology and then followed by PSOSOM in the second stage to fine tune the SOM weights. The proposed PSOSOM algorithm is compared with other two algorithms and also compare to CGASOM from the previous study using four benchmark datasets, Iris, Wine, Vowel, and Glass. The simulation results indicate that PSOSOM algorithm is able to find the better solution. Furthermore, the proposed approach has been also employed to Group Technology to cluster components into part families for a medical furniture manufacturer in Indonesia.
    Keywords: Cluster analysis, GSOM, PSO, Group Technology

    ABSTRACTi ACKNOWLEDGEMENTii CONTENTSiii LIST OF FIGURESvi LIST OF TABLESviii Chapter 1 INTRODUCTION1 1.1.Research Background1 1.2.Research Objectives2 1.3.Research Scopes and Constraints2 1.4.Research Framework3 Chapter 2 LITERATURE SURVEY5 2.1.Self-Organizing Maps (SOM)5 2.1.1.Basic concept of SOM5 2.1.2.Original Algorithm of SOM8 2.2.Growing Self-organizing Maps (GSOM)9 2.2.1.Basic Concept of GSOM9 2.2.2.New Node Initialization Rule10 2.2.3.Original Algorithm of GSOM13 2.3.Meta-Heuristic Methods15 2.3.1.Particle Swarm Optimization (PSO) Algorithm15 2.3.2.Genetics Algorithm (GA)17 2.4.Hybrid Meta-Heuristics Methods in Clustering18 Chapter 3 METHODOLOGY20 3.1.First Stage20 3.2.Second Stage22 3.2.1.PSO Algorithm23 3.2.2.PSOSOM Algorithm24 3.2.3.SOM+PSO Algorithm25 3.3.Comparison25 Chapter 4 COMPUTATIONAL RESULTS AND ANALYSIS27 4.1.Parameters Setup27 4.2.Computational Results29 4.2.1.First Stage29 4.2.2.Second Stage32 4.3.Statistical Test33 4.4.Algorithm Convergence34 Chapter 5 MODEL EVALUATION RESULTS37 5.1.Source of practical case and data processing38 5.2.The result and analysis39 5.2.1.SOM topology determination using GSOM algorithm39 5.2.2.Comparison of proposed algorithms40 5.2.3.Clustering result of practical cases41 5.2.4.Verification of clustering result44 Chapter 6 CONCLUSIONS AND FUTURE RESEARCH46 6.1.Conclusion46 6.2.Contributions46 6.3.Future Research47 REFERENCE48 Appendix I COMPUTATIONAL RESULT51 Appendix II STATISTICAL RESULT OF PROPOSED ALGORITHMS56 Appendix III GSOM OUTPUT TOPOLOGY OF STUDY CASE RESULT61 Appendix IV CASE STUDY COMPUTATIONAL RESULT68 Appendix V DETERMINING CENTROID FOR K-MEANS70

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