Abstract:
By use of the properties of ant colony algorithm and genetic algorithm, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts genetic ...Show MoreMetadata
Abstract:
By use of the properties of ant colony algorithm and genetic algorithm, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts genetic algorithm to give information pheromone to distribute. Second, it makes use of the ant colony algorithm to get several solutions through information pheromone accumulation and renewal. Finally, by using across and mutation operation of genetic algorithm, the effective solutions are obtained. Compare with the simulated annealing algorithm, the standard genetic algorithm, the standard ant colony algorithm, and statistics initial ant colony algorithm, all the 16 hybrid algorithms are proved effective. Especially the hybrid algorithm with across strategy B and mutation strategy B is a simple and effective better algorithm than others.
Published in: 2007 Chinese Control Conference
Date of Conference: 26-31 July 2007
Date Added to IEEE Xplore: 15 October 2007
ISBN Information:
Print ISSN: 1934-1768
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1.
Colorni A, Dorigo M, Maniezzo V. An investigation of some properties of an ant algorithm: Proc. Of the Parallel Problem Solving from Nature Conference (PPSN'92). Brussels, Belgium: Elsevier Publishing, 1992, 509-520.
2.
Chinese source
3.
Chinese source
4.
Gunes M, Sorges U, Bouazizi I. ARA the ant colony based routing algorithm for MANETs: Proceedings International Conference on Parallel Processing Workshops. Uuncouver, B C, Canada, 2002:79-85.
5.
Lumer E, Faieta B. Diversity and adaptation in populations of clustering ants: Proc of the 3 Conf On Simulation of Adaptive Behavior: MIT Press, 1994:499-508.
6.
Parpinelli R S, Lopes H S, Freitas. Data mining with an Ant Colony optimization algorithm[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(4):321-332.
7.
Lee Zne Jung, Lee ChouYuan, Su ShunFeng.An immunity based ant colony optimization algorithm for solving weapon-target assignment problem [J]. Applied Soft Computing Journal, 2002, 2(1):39-47.
8.
Silva De A, Ramalh R M. Ant system for the set covering problem.IEEE International Conference on Systems.Man, and Cybernetics, Tucson, AZ USA, 2001.
9.
Chinese source
10.
Chinese source
11.
Dorigo M, Maniezzo V. Colorni A. Ant system: optimization by a colony of cooperating agents [J]. IEEE Trans. On System,Man and Cybernatics,1996,26(1):28- 41.
12.
Chinese source
13.
Stutzle T, Hhoos H. The MAX-MIN ant system and local search for the traveling salesman problem: Proceedings of the IEEE Interna tional Conference on Evolutionary Computation(ICEC97). Indianapolis, USA, 1997:309-314.
14.
Chinese source
15.
Chinese source