Vehicle Path Planning by Use of SOM
This paper presents new concepts to apply Self-Organizing Maps (SOM) to vehicle path problems. It is reported SOM is capable of solving a traveling salesman problem (TSP), one of the vehicle path problems. However, it is not investigated how SOM is applied to other types of the vehicle path problems...
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| Published in | Journal of the Japan Society for Precision Engineering, Contributed Papers Vol. 72; no. 5; pp. 591 - 595 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
The Japan Society for Precision Engineering
05.05.2006
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1348-8724 1881-8722 |
| DOI | 10.2493/jspe.72.591 |
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| Abstract | This paper presents new concepts to apply Self-Organizing Maps (SOM) to vehicle path problems. It is reported SOM is capable of solving a traveling salesman problem (TSP), one of the vehicle path problems. However, it is not investigated how SOM is applied to other types of the vehicle path problems, such as the shortest path problem (SPP) and n traveling salesmen problem (n-TSP). Numerical experiments prove that SOM cannot lead to good solution when it is applied to SPP and n-TSP. To improve SOM, two multi-neuron concepts are introduced to solve these problems. Numerical experiments verify that SOM using multi-neuron leads to better solutions than the conventional SOM does. Furthermore, SOM with variable number neurons is proposed to make SOM's solution converge efficiently. |
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| AbstractList | This paper presents new concepts to apply Self-Organizing Maps (SOM) to vehicle path problems. It is reported SOM is capable of solving a traveling salesman problem (TSP), one of the vehicle path problems. However, it is not investigated how SOM is applied to other types of the vehicle path problems, such as the shortest path problem (SPP) and n traveling salesmen problem (n-TSP). Numerical experiments prove that SOM cannot lead to good solution when it is applied to SPP and n-TSP. To improve SOM, two multi-neuron concepts are introduced to solve these problems. Numerical experiments verify that SOM using multi-neuron leads to better solutions than the conventional SOM does. Furthermore, SOM with variable number neurons is proposed to make SOM's solution converge efficiently. |
| Author | KAKAZU, Yukinori FURUKAWA, Masashi WATANABE, Michiko |
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| Copyright | 2006 The Japan Society for Precision Engineering Copyright Japan Science and Technology Agency 2006 |
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| References | 4) 古川正志他3名：システム工学, コロナ社（2000）23 5) D.Watts : Small Worlds The Dynamics od Networks between Order and Randomness, Princeton University Press(2004). 8) 中野馨編著：ニューロコンピュータの基礎, コロナ社（1991） 13) 小林大祐, 渡辺美知子, 古川正志：AGV走行地図のSOMによる獲得，日本機会学会北海道支部第43回講演会講演概要集No.032-1（2003）38. 9) J.J.Grefenstette et al.: Genetic Algorithm for the TSP, Proc. Of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates(1985)160. 3) R. ベルマン他（渡辺茂監訳）：計算機のためのグラフとアルゴリズム, 共立出版社（1972） 11) TSP-JPN:http://pagetest.hp.infoseek.co.jp/m-s2-opt/m-s2-opt-frame.html 2) C. McMillan, Jr.:Mathematical Programming, John Wiley & Sons, Inc. (1970). 6) T.コホネン（特高平蔵他訳）：自己組織化マップ, シュプリンガー・フェアラーク東京（1996） 10) 當間愛晃, 遠藤 聡志, 山田孝治 : 免疫アルゴリズムのnTSPへの適用, 情報処理学会第55回全国大会講演論文集(2)(1997)453 1) 渡辺治：計算可能性・計算の複雑さ入門, 近代科学社（1992） 12) TSPLIB95: http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95 7) H.Ritter, T.Martinetz and K.Schulten: Neural Computation and Self-Organizing MapsAddison-, Wesley Publishing Company(1992). 11 12 13 1 2 3 4 5 6 7 8 9 10 |
| References_xml | – reference: 10) 當間愛晃, 遠藤 聡志, 山田孝治 : 免疫アルゴリズムのnTSPへの適用, 情報処理学会第55回全国大会講演論文集(2)(1997)453． – reference: 9) J.J.Grefenstette et al.: Genetic Algorithm for the TSP, Proc. Of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates(1985)160. – reference: 11) TSP-JPN:http://pagetest.hp.infoseek.co.jp/m-s2-opt/m-s2-opt-frame.html – reference: 12) TSPLIB95: http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/ – reference: 1) 渡辺治：計算可能性・計算の複雑さ入門, 近代科学社（1992）． – reference: 8) 中野馨編著：ニューロコンピュータの基礎, コロナ社（1991）． – reference: 4) 古川正志他3名：システム工学, コロナ社（2000）23． – reference: 6) T.コホネン（特高平蔵他訳）：自己組織化マップ, シュプリンガー・フェアラーク東京（1996）． – reference: 13) 小林大祐, 渡辺美知子, 古川正志：AGV走行地図のSOMによる獲得，日本機会学会北海道支部第43回講演会講演概要集No.032-1（2003）38. – reference: 3) R. ベルマン他（渡辺茂監訳）：計算機のためのグラフとアルゴリズム, 共立出版社（1972）． – reference: 2) C. McMillan, Jr.:Mathematical Programming, John Wiley & Sons, Inc. (1970). – reference: 5) D.Watts : Small Worlds The Dynamics od Networks between Order and Randomness, Princeton University Press(2004). – reference: 7) H.Ritter, T.Martinetz and K.Schulten: Neural Computation and Self-Organizing MapsAddison-, Wesley Publishing Company(1992). – ident: 2 – ident: 3 – ident: 5 – ident: 4 – ident: 1 – ident: 12 – ident: 11 – ident: 10 – ident: 13 – ident: 6 – ident: 9 – ident: 7 – ident: 8 |
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| Snippet | This paper presents new concepts to apply Self-Organizing Maps (SOM) to vehicle path problems. It is reported SOM is capable of solving a traveling salesman... |
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| SubjectTerms | machine learning neural network self-organizing map traveling salesman problem vehicle path problem |
| Title | Vehicle Path Planning by Use of SOM |
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