An Improved Swarm Intelligence Algorithm for Solving TSP Problem

  • Authors:

  • Yong-Qin Tao;Du-Wu Cui;Xiang-Lin Miao;Hao Chen

  • Affiliations:

  • School of Computer Science and Engineering ,Xi'an University of Technology, Xi'an 710048, China and School of Electronic and Information Engineering ,Xi'an Jiaotong University, Xi'an 710049, China;School of Computer Science and Engineering ,Xi'an University of Technology, Xi'an 710048, China;School of Electronic and Information Engineering ,Xi'an Jiaotong University, Xi'an 710049, China;School of Computer Science and Engineering ,Xi'an University of Technology, Xi'an 710048, China

  • Venue:
  • ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
  • Year:
  • 2009

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Abstract

Traveling Salesman Problem (TSP) is a typical NP-Complete problem. This paper, through finding the solution of TSP, combining the use of high-efficiency gene regulatory algorithm , particle swarm optimization and ant colony optimization, proposes a kind of improved swarm intelligence algorithm GRPSAC. The GRPSAC overcomes the disadvantages of several algorithms through the use of the crossover, the mutation and the gene regulation. The experimental results indicate that GRPSAC not only has a highefficiency, but also induces better optimal results