Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
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Hybrid Parallel Genetic Algorithm for Traveling Salesman Problem

순회 판매원 문제를 위한 하이브리드 병렬 유전자 알고리즘

  • Kim, Ki-Tae (Korea National Defense University Department of Operations Research) ;
  • Jeo, Geon-Wook (Korea National Defense University Department of Operations Research)
  • Received : 2011.07.19
  • Accepted : 2011.09.22
  • Published : 2011.09.30
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Abstract

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

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References

  1. 김여근.윤복식.이상복, 메타 휴리스틱, 영지문화사, 2000.
  2. 이상헌, 메타 네트워크 이론 및 응용, 국방대학교, 2003.
  3. M. Dorigo and L. M. Gambardella, Ant Colonies for the Traveling Salesman Problem, Biosystems, Vol.43, No.2, pp.73-81, 1997. https://doi.org/10.1016/S0303-2647(97)01708-5
  4. H. Fan, Discrete Particle Swarm Optimization for TSP Based on Neighborhood, Journal of Computational Information Systems, Vol.6, No.10, pp.3407-3414, 2010.
  5. M. Gendreau, A. Hertz, and G. Laporte, New Insertion and Postoptimization Procedures for the Traveling Salesman Problem, Operations Research, Vol. 40, No. 6, pp.1086-1094, 1992. https://doi.org/10.1287/opre.40.6.1086
  6. B. Golden, L. Bodin, T. Doyle, and W. Stewart, Jr., Approzimate Traveling Salesman Algorithms, Operations Research, Vol. 28, No.3. pp.694-711, 1980. https://doi.org/10.1287/opre.28.3.694
  7. J. J. Grefenstette, Parallel Adaptive Algorithm for Function Optimization, Technical Report No.CS-81-19, Vanderbit University, Computer Scinece Department, Nashville, 1981.
  8. Y. He, G. Liu, and Y. Qiu, A Parallel Tabu Search Algorithm Based on Partitioning Principle for TSPs, International Journal of Computer Science and Network Security, Vol.6, No.8, pp.146-150, 2006.
  9. J. H. Holland, Adaption in Natural and Artificial Systems, University of Michigan Press, 1975.
  10. E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, and D. B. Shmoys, The Traveling Salesman Problem, John Wiley & Sons, Inc., NY, 1985.
  11. A. R. Malisia and H. R. Tizhoosh, Applying Opposition Based Ideas to the Ant Colony System, Proceedings of IEEE Swarm Intelligence Symposium(SIS-2007), pp.182-189, Hawaii, USA, 2007.
  12. C. E. Miller, A. W. Tucker, and R. A. Zemlin, Integer grogramming formulation of traveling salesman problems, Journal of Association for Computing Machinery, Vol.7, No.4, pp.326-329, 1960. https://doi.org/10.1145/321043.321046
  13. C. F. Tsai and C. W. Tsai, A New Approach for Solving Large Traveling Salesman Problem using Evolutionary Ant Rules, Proceedings of International Joint Conference on Neural Networks, 2002.
  14. TSPLIB, Traveling Salesman Problem Library, http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/,2011.