Solving large scale traveling salesman problems by chaotic neurodynamics

  • Authors:

  • Mikio Hasegawa;Tohru Ikeguchi;Kazuyuki Aihara

  • Affiliations:

  • Wireless Networks Integration Group, Wireless Communications Division, Communications Research Laboratory, Independent Administrative Institution, Yokosuka-shi, Kanagawa, Japan;Department of Information and Numerical Sciences, Graduate School of Science and Engineering, Saitama University, Saitama, Japan and Faculty of Engineering, Department of Information and Computer ...;Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan and CREST, Japan Science and Technology Corporation (JST), Japan

  • Venue:
  • Neural Networks
  • Year:
  • 2002

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Abstract

We propose a novel approach for solving large scale traveling salesman problems (TSPs) by chaotic dynamics. First, we realize the tabu search on a neural network, by utilizing the refractory effects as the tabu effects. Then, we extend it to a chaotic neural network version. We propose two types of chaotic searching methods, which are based on two different tabu searches. While the first one requires neurons of the order of n2 for an n-city TSP, the second one requires only n neurons. Moreover, an automatic parameter tuning method of our chaotic neural network is presented for easy application to various problems. Last, we show that our method with n neurons is applicable to large TSPs such as an 85,900-city problem and exhibits better performance than the conventional stochastic searches and the tabu searches.