Adaptive ant colony system algorithm for continuous-space optimization problems

Journal of Zhejiang University: Science

Volumn 4, Issue 1, 2003, Pages 40-46

  Li, Yan Jun ^a   Wu, Tie Jun ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Ant colony algorithm; Continuous space optimization; Pheromone update strategy

Indexed keywords

BENCHMARKING; EVOLUTIONARY ALGORITHMS; GLOBAL OPTIMIZATION; OPTIMIZATION;

ANT COLONY ALGORITHM; CONTINUOUS SPACE OPTIMIZATION; PHEROMONE UPDATE STRATEGY;

ADAPTIVE ALGORITHMS;

PHEROMONE;

ALGORITHM; ANIMAL; ANIMAL BEHAVIOR; ANT; ARTICLE; BIOLOGICAL MODEL; PHYSIOLOGY;

ALGORITHMS; ANIMALS; ANTS; BEHAVIOR, ANIMAL; MODELS, BIOLOGICAL; PHEROMONES;

EID: 0037503045     PISSN: 10093095     EISSN: None     Source Type: Journal    
DOI: 10.1631/jzus.2003.0040     Document Type: Article

References (13)

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 Stutzle 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. Miehalewiez, 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. Stutzle, 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.