Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2003.10B.7.751

Multi Colony Ant Model using Positive.Negative Interaction between Colonies  

Lee, Seung-Gwan (경희대학교 대학원 전자계산공학과)
Chung, Tae-Choong (경희대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.10B, no.7, 2003 , pp. 751-756 More about this Journal
Abstract
Ant Colony Optimization (ACO) is new meta heuristics method to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was firstly proposed for tackling the well known Traveling Salesman Problem (TSP) . In this paper, we introduce Multi Colony Ant Model that achieve positive interaction and negative interaction through Intensification and Diversification to improve original ACS performance. This algorithm is a method to solve problem through interaction between ACS groups that consist of some agent colonies to solve TSP problem. In this paper, we apply this proposed method to TSP problem and evaluates previous method and comparison for the performance and we wish to certify that qualitative level of problem solution is excellent.
Keywords
Ant Colony Optimization:ACO; Ant System:AS; Ant Colony System:ACS; Intensification; Diversification; Meta Heuristics;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Colorni, M. Dorigo and V. Maniezzo, 'An investigation of some properties of an ant algorithm,' Proceedings of the Parallel Problem Solving from Nature Conference(PPSn 92), R. Manner and B. Manderick (Eds.), Elsevier Publishing, pp.509-520, 1992
2 A. Colorni, M. Dorigo and V. Maniezzo, 'Distributed optimization by ant colonies,' Proceedings of ECAL91 - European Conference of Artificial Life, Paris, France, F. Varela and P. Bourgine(Eds.), Elsevier Publishing, pp.134-144, 1991
3 B. Freisleben and P. Merz, 'Genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems,' Proceedings of IEEE International Conference of Evolutionary Computation, IEEE-EC 96, IEEE Press, pp. 616-621, 1996   DOI
4 L. M. Gambardella and M. Dorigo, 'Ant Colony System : A Cooperative Learning approach to the Traveling Salesman Problem,' IEEE Transactions on Evolutionary Computation, Vol.1, No.1, 1997   DOI
5 L. M. Gambardella and M. Dorigo, 'Solving symmetric and asymmetric TSPs by ant colonies,' Proceedings of IEEE International Conference of Evolutionary Computaion, IEEE-EC 96, IEEE Press, pp.622-627, 1996   DOI
6 M. Drigo, V. Maniezzo and A. Colorni, 'The ant system : optimization by a colony of cooperation agents,' IEEE Transactions of Systems, Man, and Cybernetics-Part B, Vol.26, No.2, pp.29-41, 1996   DOI
7 S. Lin and B. W. Kernighan, 'An effective Heuristic algorithm for the traveling salesman problem,' Operations Research, Vol.21, pp.498-516, 1973   DOI   ScienceOn
8 http://www.iwr.uniheidelberg.de/groups/comopt/software/TSPLIB95/
9 L. M. Gambardella and M. Dorigo, 'Ant-Q : a reinforcement learning approach to the traveling salesman problem,' Proceedings of ML-95, Twelfth International Conference on Machine Learning, A. Prieditis and S. Russell (Eds.), Morgan Kaufmann, pp.252-260, 1995
10 M. Dorigo and G. D. Caro, 'Ant Algorithms for Discrete Optimization,' Artificial Life, Vol.5, No.3, pp.137-172, 1999   DOI   ScienceOn
11 M. Dorigo and L. M. Gambardella, 'Ant Colonies for the Traveling Salesman Problem,' BioSystems, 43, pp.73-81, 1997   DOI   ScienceOn