- 著者
-
Rong-Long WANG
Xiao-Fan ZHOU
Kozo OKAZAKI
- 出版者
- 一般社団法人 電子情報通信学会
- 雑誌
- IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (ISSN:09168508)
- 巻号頁・発行日
- vol.E92.A, no.5, pp.1368-1372, 2009-05-01 (Released:2009-05-01)
- 参考文献数
- 15
- 被引用文献数
-
1
3
1
Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.