Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Ant Colony System for solving the traveling Salesman Problem Considering the Overlapping Edge of Global Best Path

순회 외판원 문제를 풀기 위한 전역 최적 경로의 중복 간선을 고려한 개미 집단 시스템

  • Lee, Seung-Gwan (The College of Liberal Arts, Kyung Hee University) ;
  • Kang, Myung-Ju (Dept. of Computer Game, ChungKang College of Cultural Industries)
  • 이승관 (경희대학교 국제캠퍼스 학부대학) ;
  • 강명주 (청강문화산업대학 컴퓨터게임과)
  • Received : 2011.01.11
  • Accepted : 2011.02.07
  • Published : 2011.03.31
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Abstract

Ant Colony System is a new meta heuristics algorithms to solve hard combinatorial optimization problems. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the searching method to consider the overlapping edge of the global best path of the previous and the current. This method is that we first determine the overlapping edge of the global best path of the previous and the current will be configured likely the optimal path. And, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath and ACS-Iter algorithms.

개미 집단 시스템은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법으로, 그리디 탐색뿐만 아니라 긍정적 피드백을 사용한 모집단에 근거한 접근법으로 순회 판매원 문제를 풀기 위해 처음으로 제안되었다. 본 논문에서는 이전 전역 최적 경로와 현재 전역 최적 경로의 중복 간선을 고려한 탐색 방법을 제안하였다. 이 방법은 이전전역 최적 경로와 현재 전역 최적 경로에서의 중복 간선은 최적 경로로 구성될 가능성이 높다고 판단하고, 해당 중복 간선에 대해 페로몬을 강화시켜 최적 경로를 구성할 확률을 높이게 하였다. 그리고, 실험을 통해 ACS-3-opt 알고리즘, ACS-Subpath 알고리즘, ACS-Iter 알고리즘에 비해 최적 경로 탐색 및 평균 최적 경로 탐색의 성능이 우수함을 보여 주었다.

Keywords

References

  1. L.M. Gambardella and M. Dorigo, "Ant Colony System: A Cooperative Learning approach to the Traveling Salesman Problem" IEEE Transactions on Evolutionary Computation, v.1, no.1, pp.53-66, 1997. https://doi.org/10.1109/4235.585892
  2. M. Dorigo and L.M. Gambardella. "Ant Colonies for the Traveling Salesman Problem". BioSystems, v.43, pp.73-81. 1997. https://doi.org/10.1016/S0303-2647(97)01708-5
  3. M. Dorigo, L.M. Gambardella, M. Middendorf and T. Stutzle, "Ant Colony Optimization", IEEE Transactions on Evolutionary Computation, v.6, no.4, 2002.
  4. M. Dorigo and C. Blum. "Ant colony optimization theory: A survey", Theoretical Computer Science, 344(2-3), pp.243-278, 2005. https://doi.org/10.1016/j.tcs.2005.05.020
  5. M. Dorigo, M. Birattari and T. Stutzle, "Ant Colony Optimization - Artificial Ants as a Computational Intelligence Technique", IEEE Computational Intelligence Magazine, v.1, no.4, pp.28-39, 2006. https://doi.org/10.1109/CI-M.2006.248054
  6. M. Dorigo and T. Stutzle, "The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances", Handbook of Metaheuristics, 2002.
  7. M. Dorigo and K. Socha, "An Introduction to Ant Colony Optimization", Approximation Algorithms and Metaheuristics, CRC Press, 2007.
  8. I.K Kim and M.Y Youn, "Improved Ant Colony System for the Traveling Salesman Problem", The KIPS transactions. Part B, v.12, no.7, pp.823-828, 2005. https://doi.org/10.3745/KIPSTB.2005.12B.7.823
  9. S.G Lee, "Ant Colony System Considering the Iteration Search Frequency that the Global Optimal Path does not Improved", Journal of The Korea Society of Computer and Information, v.14, no.1, pp.9-15, 2009.
  10. http://elib.zib.de/pub/Packages/mp-testdata/tsp/tsplib /tsplib.html
  11. L. M. Gambardella and M. Dorigo, "HAS-SOP: An Hybrid Ant System for the Sequential Ordering Problem," Tech. Rep. No. IDSIA 97-11, IDSIA, Lugano, Switzerland, 1997.
  12. V. Maniezzo and A. Colorni. "The Ant System Applied to the Quadratic Assignment Problem," IEEE Transactions on Knowledge and Data Engineering, v.11, no.5, pp.769-778, 1999. https://doi.org/10.1109/69.806935
  13. B. Bullnheimer, R.F. Hartl and C. Strauss. "Applying the Ant System to the Vehicle Routing Problem." Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, Kluwer:Boston. 1999.
  14. A. Colorni, M. Dorigo, V. Maniezzo and M. Trubian. "Ant system for Job-shop Scheduling." JORBEL - Belgian Journal of Operations Research, Statistics and Computer Science, 34(1): pp.39-53. 1994
  15. D. Costa and A. Hertz. "Ants Can Colour Graphs." Journal of the Operational Research Society, 48, pp.295-305. 1997. https://doi.org/10.1057/palgrave.jors.2600357
  16. G. Di Caro and M. Dorigo "AntNet: Distributed Stigmergetic Control for Communications Networks." Journal of Artificial Intelligence Research (JAIR), 9:317-365. 1998.
  17. S.G Lee, "Elite Ant System for Solving Multicast Routing Problem", Journal of The Korea Society of Computer and Information, v.13, no.3, pp.147-152, 2008.

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