Abstract
Logistics route distribution optimization problem (LRDOP) belongs to traveling salesman problem (TSP), but it not only has higher requirements on the running efficiency of the path planning algorithm, but also is easier to fall into local optimum. Ant colony optimization (ACO) is one of the dominant algorithms for solving TSP. In ACO, \(\alpha \) and \(\beta \) parameters are critical and specific. Symbiotic organisms search (SOS) is a non-parametric algorithm, so the \(\alpha \) and \(\beta \) parameters of the ACO can be dynamically optimized by using SOS. In this paper, we introduce a hybrid ant colony optimization SOSACOp, which uses a mixture of ACO and SOS, and adjusts the results by using local optimization strategies and another pheromone updating rules. Experimental results show that SOSACOp has better comprehensive performance than ACO.
This work is supported by Key R &D project of China National Tobacco Corporation No. 110202102031 and Project of Science and Technology Project of Hubei Tobacco Company No. 027Y2021-046.
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Zhang, C., Cai, Y., Hu, P., Quan, P., Song, W. (2022). Logistics Distribution Route Optimization Using Hybrid Ant Colony Optimization Algorithm. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_45
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