| 摘 要: 为了有效求解旅行商问题，本文提出了一种基于T分布的改进蚁群算法。针对基本蚁群算法易陷入局部最 优、寻优精度低等缺陷，在优化过程中，在信息素更新原则上，引入T分布，有益于基本蚁群算法弥补其不足。在基本 蚁群算法中增加了信息素的突变，使得蚂蚁群的多样性提高，从而跳出局部最优的限制。与此同时，T-ACO算法在旅 行商问题搜寻精度与收敛速度方面也得到了提高。对T-ACO求解旅行商问题的性能进行了实验仿真，实验分析表明， T-ACO算法有更好的寻优能力。 |
| 关键词: T分布；蚁群算法；旅行商问题；优化 |
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中图分类号: TP391.9
文献标识码: A
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| 基金项目: 天津市企业科技特派员项目“低碳规限下的京津冀智慧物流优化关键技术研究”(课题编号: 19JCTPJC51600)；2019年大学生创新创业训练计划项目“京津冀地区电 动汽车充电设施规划的研究”(项目编号:201910069010)研究成果. |
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| Optimization of Traveling Salesman Problem Solution Based on T-ACO Algorithm |
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FEI Teng,ZHAO Bin,HUANG Jundong,LIU Zetian1,2
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1.( 1.School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China;2.
2.Chengde No.12 Middle School, Chengde 067000, China)
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| Abstract: In order to effectively solve the traveling salesman problem,this paper proposes an improved ant colony algorithm based on T distribution.Aiming at solving the defects that the basic ant colony algorithm easily falls into local optimum and has low optimization precision,T-distribution is introduced in the optimization process based on the principle of pheromone update,which helps to make up for shortcomings of basic ant colony algorithm.The mutation of pheromone is added to the basic ant colony algorithm in order to improve the diversity of ant colony,so that the basic ant colony algorithm can jump out of the local optimal limit.At the same time,T-ACO algorithm improves the search accuracy and convergence speed of solving the traveling salesman problem.Experimental analysis shows that T-ACO algorithm has better optimization capability. |
| Keywords: T distribution;ant colony algorithm;traveling salesman problem;optimization |
