IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Regular Section
Ant Colony Optimization with Genetic Operation and Its Application to Traveling Salesman Problem
Rong-Long WANGXiao-Fan ZHOUKozo OKAZAKI
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2009 年 E92.A 巻 5 号 p. 1368-1372

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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.

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© 2009 The Institute of Electronics, Information and Communication Engineers
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