Abstract
This paper presents a new method called hybrid ant bee colony optimization (HABCO) for solving traveling salesman problem which combines ant colony system (ACS), bee colony optimization (BCO) and ELU-Ants. The agents, called ant-bees, are grouped into three types, scout, follower, recruiter at each stages as BCO algorithm. However, constructing tours such as choosing nodes, and updating pheromone are built by ACS method. To evaluate the performance of the proposed algorithm, HABCO is performed on several benchmark datasets and compared to ACS and BCO. The experimental results show that HABCO achieves the better solution, either with or without 2opt.
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Girsang, A.S., Tsai, CW., Yang, CS. (2014). A Hybrid Ant-Bee Colony Optimization for Solving Traveling Salesman Problem with Competitive Agents. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_95
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DOI: https://doi.org/10.1007/978-3-642-40675-1_95
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