Title :
Application of Improved Ant Colony Algorithm
Author :
Shi, Hongyan ; Bei, Zhaoyu
Author_Institution :
Sch. of Inf. Sci. & Eng., Shenyang Univ. of Technol., Shenyang
Abstract :
A stochastic optimization algorithm is proposed by combining ant colony (ACO) algorithm with artificial fish-swarm algorithm (AFSA) for solving continuous space optimization problems. The algorithm is improved with the rapid search capability of AFSA and the good search characteristics of ACO, and the convergence speed of the presented algorithm is also improved for avoiding being trapped in local optimization. The improved algorithm has been tested for varieties of functions. And the algorithm can handle these optimization problems very well.
Keywords :
optimisation; stochastic processes; artificial fish-swarm algorithm; improved ant colony algorithm; stochastic optimization algorithm; Ant colony optimization; Cities and towns; Computer applications; Convergence; Euclidean distance; Information science; Space technology; Stochastic processes; Testing; Traveling salesman problems;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.75
