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Journal of Zhejiang University SCIENCE A 2003 Vol.4 No.1 P.40-46

http://doi.org/10.1631/jzus.2003.0040


An adaptive ant colony system algorithm for continuous-space optimization problems


Author(s):  Li Yan-jun, Wu Tie-jun

Affiliation(s):  Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yjlee@iipc.zju.edu.cn

Key Words:  Ant colony algorithm, Continuous-space optimization, Pheromone update strategy


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Li Yan-jun, Wu Tie-jun. An adaptive ant colony system algorithm for continuous-space optimization problems[J]. Journal of Zhejiang University Science A, 2003, 4(1): 40-46.

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Abstract: 
ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

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Reference

[1]Bilchev, G. A. and Parmee, I. C., 1995. The ant colony metaphor for searching continuous design spaces. Lecture Notes in Computer Science, 993: 25-39.

[2]Dorigo, M., 1992. Optimization, learning, and natural algorithms. Ph. D. Thesis, Dip Elettronica, Politecnico di Milano, Italy.

[3]Dorigo, M., Bonabeau, E. and Theraulaz, G., 2000. Ant algorithms and stigmergy. Future Generation Computer Systems, 16: 851-871.

[4]Dorigo, M., Maniezzo, V. and Colorni, A., 1996. Ant system: optimization by a colony of cooperating agents. IEEE Trans. On Systems, Man and Cybernetics, 26(1): 28-41.

[5]Dorigo, M., Caro D. G. and Stuzle T., 2000. Ant algorithms. Future Generation Computer Systems, 16: p.V-VII.

[6]Gutjahr, W. J., 2000. A graph-based ant system and its convergence. Future Generation Computer System, 16: 837-888.

[7]Hertz, A. and Kobler, D., 2000. A framework for the description of evolutionary algorithms. European Journal of Operational Research, 126: 1-12.

[8]Michalewicz, Z., 1996. Genetic algorithms +date structures = evolution programs. Springer -Verlag Berlin Heidelberg.

[9]Li, Y., Wu, T.-J., 2002. A nested ant colony algorithm for hybrid production scheduling. Proceedings of the American Control Conference. Anchorage, AK: 1123-1128.

[10]Preux, Ph. and Talbi, E.-G., 1999. Towards hybrid evolutionary algorithms. Intl. Trans. in Operational Research, 6: 557-570.

[11]Song, Y. H., Chou, C. S. and Stonham, T. J., 1999. Combined heat and power economic dispatch by improved ant colony search algorithm. Electric Power Systems Research, 52: 115-121.

[12]Stuzle, T. and Hoos, H. H., 2000. Max-Min ant system. Future Generation Computer Systems, 16: 889-914.

[13]Zhang, J., Gao, Q. and Xu, X., 2000. A self-adaptive ant colony algorithm. Control theory and applications, 17(1): 1-8.

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