An improved ant colony optimization applied to attributes reduction

Ting-quan Deng

Cheng-dong Yang

Yue-tong Zhang

Xin-xia Wang

pp. 1-6

in: Bing-yuan Cao, Cheng-yi Zhang, Tai-fu Li (eds), Fuzzy information and Engineering I, Berlin, Springer, 2009

Abstract

Attribute reduction problem (ARP) in rough set theory is an NP-hard problem, which is difficult to use fast traditional method to solve. In this paper, we discuss about the difference between the traveling salesman problems (TSP) and the ARP, and then we bring up a new state transition probability formula and a new pheromone traps increment formula of ant colony optimization. The results demonstrate that the improved ant colony optimization is better than initial ant colony optimization used in attribute reduction and more suitable for ARP.