Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

Quan-Li Xu; Yu-Wei Cao; Kun Yang

doi:10.1117/12.2305838

6 March 2018 Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

Quan-Li Xu, Yu-Wei Cao, Kun Yang

Author Affiliations +

Proceedings Volume 10610, MIPPR 2017: Parallel Processing of Images and Optimization Techniques; and Medical Imaging; 1061009 (2018) https://doi.org/10.1117/12.2305838
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

Citation Download Citation

Quan-Li Xu, Yu-Wei Cao, and Kun Yang "Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem", Proc. SPIE 10610, MIPPR 2017: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, 1061009 (6 March 2018); https://doi.org/10.1117/12.2305838

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