An adaptive ant colony system algorithm for continuous-space optimization problems.
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.
Li
Yan-jun
YJ
Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China. yjlee@iipc.zju.edu.cn
Wu
Tie-jun
TJ
D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology*; D001522:Behavior, Animal; D008954:Models, Biological; D010675:Pheromones / Q000502:physiology
D010675:Pheromones