A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem

Authors:
Ling-Ning Xing;Ying-Wu Chen;Ke-Wei Yang;Feng Hou;Xue-Shi Shen;Huai-Ping Cai
Affiliations:
College of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China;College of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China;College of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China;College of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China;College of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China;College of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

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Abstract

The asymmetric traveling salesman problem (ATSP) appears in various applications. Although there are several heuristic approaches to its solution, the problem is still a difficult combinatorial optimization problem. This work proposes a novel hybrid approach specialized for the ATSP. The proposed method incorporates an improved genetic algorithm (IGA) and some optimization strategies that contribute to its effectiveness. In the IGA, both the crossover operation and the mutation operation are improved by selecting the optimum from a set of solutions. Three strategies: immigration, local optimization and global optimization are established based on several empirical optimization strategies to improve the evolution of the IGA. Computational experiments are conducted on 16 ATSP instances available in the TSPLIB (traveling salesman problem library). The comparative study shows that our proposed approach outperforms several other published algorithms.