光通信研究Issue(2)：105-109,5.DOI:10.13756/j.gtxyj.2025.240037
并行化的多目标优化海缆路由规划算法研究
Research on Parallel Multi-objective Optimal Submarine Cable Route Planning Algorithm
摘要
Abstract
[Objective]In order to solve the problem that the traditional Ant Colony Optimization(ACO)algorithm updates the same map,resulting in the inability of parallel planning,a parallel multi-objective optimization submarine cable route planning al-gorithm is proposed in this paper,which realizes the precise planning of local areas.[Methods]In this paper,the grid map of the target sea area is divided into multiple grid subgraphs by the idea of divide and conquer,and a parallel multi-objective optimization submarine cable route algorithm model is established,and the key parameters of the model are optimized.Then,the Parallel Ant Colony Optimization(PACO)algorithm is used to carry out the submarine cable route planning under the optimal model parame-ters,and the submarine cable route scheme solved by Pareto frontier is counted.[Results]The simulation results show that the parallel multi-objective optimization model obtains the best search ability and efficiency when the number of blocks is 6 and the size of ant colony is 150.The PACO algorithm can save 33.9%of the cost of submarine cable route compared with the traditional ACO algorithm under the same risk conditions,and the cost of routes is smaller than the traditional ant colony algorithm.The maximum cost of routes is also reduced by 20.6%compared with the minimum cost of the traditional ACO algorithm,and the cor-responding risk is reduced by 65.8%.[Conclusion]In multi-objective submarine cable route planning,compared to the traditional ACO algorithm,the PACO algorithm not only achieves better planning results but also improves computational efficiency by at least 8 times.关键词
海缆路由规划/并行蚁群优化算法/多目标优化Key words
submarine cable route planning/PACO algorithm/multi-objective optimization分类
信息技术与安全科学引用本文复制引用
蒋佳芮,赵赞善,段茂生,高冠军..并行化的多目标优化海缆路由规划算法研究[J].光通信研究,2025,(2):105-109,5.基金项目
国家自然科学基金资助项目(62371064) （62371064）
国家重点研发计划资助项目(2022YFB2903303) （2022YFB2903303）
北京市自然科学基金资助项目(4232050) （4232050）
