光通信技术2025，Vol.49Issue(3)：10-15,6.DOI:10.13921/j.cnki.issn1002-5561.2025.03.002
机器学习辅助的多目标优化海底光缆路由规划研究
Research on multi-objective optimization of submarine optical fiber route planning assisted by machine learning
摘要
Abstract
To improve the global optimization capability of submarine cable routing planning algorithm,reduce cumulative costs and risks,and improve algorithm efficiency,a machine learning assisted(MLA)multi-objective optimization algorithm for sub-marine cable routing planning is proposed.Utilizing the advantages of reinforcement learning,MLA autonomously iterates learn-ing,synchronously optimizes costs and risks,considers parameters such as seabed topography and water depth,and adopts Pareto frontier as the convergence evaluation criterion.It is compared and verified with traditional ant colony optimization(ACO)algo-rithm.The experimental results show that under the same risk level,the algorithm can reduce the laying cost by 27.45％,and its optimal solution cumulative risk is only 25％of the ACO algorithm,and the convergence speed is improved by more than 330 times.In addition,most of its Pareto solutions are located at the forefront,which is significantly better than the discrete distribu-tion of the ACO algorithm solution set.关键词
海底光缆路由规划/多目标优化/强化学习/Q学习/海缆系统经济性优化/海缆系统生存性优化Key words
submarine optical fiber route planning/multi-objective optimization/reinforcement learning/Q-learning/economic optimization of submarine cable system/survivality optimization of submarine cable system分类
电子信息工程引用本文复制引用
赵赞善,高冠军,甘维明,王皓宇,段茂生,康达..机器学习辅助的多目标优化海底光缆路由规划研究[J].光通信技术,2025,49(3):10-15,6.基金项目
国家重点研发计划项目(2022YFB2903303)资助 （2022YFB2903303）
国家自然科学基金项目(62371064)资助. （62371064）
