| Title: | An adaptive ant colony system algorithm for continuous-space optimization problems |
| Address: | "Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China. yjlee@iipc.zju.edu.cn" |
| ISSN/ISBN: | 1009-3095 (Print) 1009-3095 (Linking) |
| 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" |
| Keywords: | "*Algorithms Animals Ants/*physiology Behavior, Animal *Models, Biological Pheromones/physiology;" |
| Notes: | "MedlineLi, Yan-jun Wu, Tie-jun eng Research Support, Non-U.S. Gov't China 2003/03/27 J Zhejiang Univ Sci. 2003 Jan-Feb; 4(1):40-6. doi: 10.1631/jzus.2003.0040" |
