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Analysis of Parameter Estimation and Optimization Application of Ant Colony Algorithm in Vehicle Routing Problem

机译:蚁群算法在车辆路径选择中的参数估计分析及优化应用

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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.
机译:蚁群优化(ACO)是目前使用最广泛的人工智能算法。该研究介绍了ACO算法解决车辆路径问题(VRP)的原理和数学模型,设计了基于ACO的车辆路径优化模型,然后使用c ++编程语言开发了车辆路径优化仿真系统,对ACO的三个关键参数进行了敏感性分析,估计和改进。结果表明,本文设计的ACO算法可以有效地解决VRP的合理规划和优化问题,关键参数的不同值对算法的性能和优化效果有重大影响,改进后的算法不易实现。局部过早收敛,具有很好的鲁棒性。

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