Hybrid chromosome genetic algorithm for generalized traveling salesman problems

Authors:
Han Huang;Xiaowei Yang;Zhifeng Hao;Chunguo Wu;Yanchun Liang;Xi Zhao
Affiliations:
College of Computer Science and Engineering, South China University of Technology, Guangzhou, P.R. China;College of Mathematical Science, South China University of Technology, Guangzhou, P.R. China;College of Mathematical Science, South China University of Technology, Guangzhou, P.R. China;College of Computer Science and Technology, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Changchun, P.R. China;College of Mathematical Science, South China University of Technology, Guangzhou, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

Generalized Traveling Salesman Problem (GTSP) is one of the challenging combinatorial optimization problems in a lot of applications. In general, GTSP is more complex than Traveling Salesman Problem (TSP). In this paper, a novel hybrid chromosome genetic algorithm (HCGA), in which the hybrid binary and integer codes are adopted, is proposed as an improvement of generalized chromosome genetic algorithm (GCGA). In order to examine the effectiveness of HCGA, 16 benchmark problems are simulated. The experimental results show that HCGA can perform better than GCGA does in solving GTSP.