A Learning Algorithm of Elastic Net for Multiple Traveling Salesmen Problem

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The Multiple Traveling Salesmen Problem (MTSP) is extension of the Traveling Salesman Problem (TSP). This problem is widely applied to many real routing and scheduling problems. This paper proposes a gradient ascent learning algorithm of the elastic net approach for the MTSP. The learning model has two phases: an elastic net phase, and a gradient ascent phase. The elastic net phase tries to find the minimum of total distances. This procedure is equivalent to gradient descent of an energy function, and leads to a local minimum of energy that represents a good solution to the problem. Once the elastic net gets stuck in local minima, the gradient ascent phase attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. Thus, these two phases are iterated until the elastic net gets out of local minima. The simulations are conducted on a series of standard data in order to investigate the performance of the proposed algorithm. The proposed algorithm is shown to be capable of escaping from the elastic net local minima and generating superior solution in all instances compared to the original elastic net.

収録刊行物

  • 電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society

    電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society 124(6), 1312-1318, 2004-06-01

    一般社団法人 電気学会

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各種コード

  • NII論文ID(NAID)
    10013069883
  • NII書誌ID(NCID)
    AN10065950
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    03854221
  • NDL 記事登録ID
    6972463
  • NDL 雑誌分類
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL 請求記号
    Z16-795
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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