Particle swarm optimization-based algorithms for TSP and generalized TSP

  • Authors:

  • X. H. Shi;Y. C. Liang;H. P. Lee;C. Lu;Q. X. Wang

  • Affiliations:

  • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China and Institute of ...;Institute of High Performance Computing, Singapore 117528, Singapore and Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 119260, Singapore;Institute of High Performance Computing, Singapore 117528, Singapore;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China

  • Venue:
  • Information Processing Letters
  • Year:
  • 2007

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Abstract

A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented. An uncertain searching strategy and a crossover eliminated technique are used to accelerate the convergence speed. Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by employing the generalized chromosome. Two local search techniques are used to speed up the convergence. Numerical results show the effectiveness of the proposed algorithms.