Abstract
Traveling salesman problem (TSP) is a renowned NP-hard combinatorial optimization model which widely studied in the operation research community, such as transportation, logistics and industries areas. To address the problem effectively and efficiently, in this paper, a new meta-heuristic method, named hybrid learning artificial bee colony, is proposed based on the simply yet powerful swarm intelligence method, artificial bee colony algorithm. In HLABC, two different learning strategies are adopted in the employed bee phase and the onlooker bee phase. The updating mechanism for food source position is enhanced by employing global best food source. Experimental results on TSP problems with various city sizes indicate the effectiveness of the proposed algorithm.
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Acknowledgment
This work was supported by the national natural science foundation of china (71501132, 71571120 and 71371127), and the Natural Science Foundation of Guangdong Province (2016A030310067 and 2015A030313556).
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Hu, G., Chu, X., Niu, B., Li, L., Lin, D., Liu, Y. (2016). An Augmented Artificial Bee Colony with Hybrid Learning for Traveling Salesman Problem. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_63
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