智能融合算法反转变异策略优化远洋气象航线

Intelligent Fusion Algorithm with Reverse Mutation Strategy for Optimizing Ocean Meteorological Route Planning

摘要

针对传统启发式算法在求解船舶远洋气象航线规划时, 存在收敛速度迟缓易陷入局部最优、 性能高度依赖参数设置的问题, 构建了基于智能优化算法改进的航线规划框架。 通过融合遗传、 蚁群与模拟退火算法的优势, 提出反转变异与信息素融合变异策略, 并设计基于适应度的横向动态调整与基于模拟退火的纵向动态调整遗传概率机制, 该策略提升了算法的全局寻优能力与收敛效率。 仿真对比试验表明, 相较于传统算法, 融合算法路径长度缩短 11.7% ~ 36.7%, 航行时间减少 12.2% ~ 37.4%, 验证了其在复杂气象环境下的优化效能。

Abstract

To address the limitations of traditional heuristic algorithms in solving the ship ocean weather routing problem, namely slow convergence, susceptibility to local optima, and high dependence on parameter settings, an improved route planning framework is constructed based on intelligent optimization algorithms. By integrating the advantages of genetic algorithm, ant colony algorithm and simulated annealing algorithm, a reversal mutation and pheromone fusion mutation strategy is proposed, and a horizontal dynamic adjustment mechanism is designed based on fitness and a vertical dynamic adjustment mechanism based on simulated annealing for genetic probabilities. These strategies enhance the global optimization capability and convergence efficiency of the algorithm. Comparative simulation experiments show that, compared to traditional algorithms, the proposed fusion algorithm shortens the path length by 11.7% ~ 36.7% and reduces the navigation time by 12.2% ~ 37.4%, verifying its optimization performance in complex meteorological environments.

关键词

路径规划 / 气象航线 / 遗传算法 / 蚁群算法

Key words

path planning / meteorological route / genetic algorithm / ant colony algorithm

参考文献

[1] ZHAO W, WANG Y, ZHANG Z, et al. Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization-Genetic Algorithm[J]. Journal of Marine Science and Engineering, 2021, 9(4): 357-372.

[2] ZIS T P, PSARAFTIS H N, DING L. Ship Weather Routing: A Taxonomy and Survey [J/OL]. Ocean Engineering, 2020, 213: 107697 [2026-05-15]. https://doi.org/10.1016/j.oceaneng.2020.107697.

[3] CHAUVIN C, LARDJANE S, MOREL G, et al. Human and Organisational Factors in Maritime Accidents: Analysis of Collisions at Sea Using the HFACS[J]. Accident Analysis & Prevention, 2013, 59: 26-37.

[4] 孙季红. 基于改进粒子群算法的远洋气象航线规划研究[D]. 长春: 吉林大学, 2022.

[5] SUN J H, Research on Ocean Meteorological Route Planning Based on Improved Particle Swarm Optimization Algorithm[D]. Changchun: Jilin University, 2022.

[6] WANG Y, QIAN L, HONG M, et al. Multi-Objective Route Planning Model for Ocean-Going Ships Based on Bidirectional A-Star Algorithm Considering Meteorological Risk and IMO Guidelines[J]. Applied Sciences, 2024, 14(17): 244-261.

[7] TAM C, BUCKNALL R, GREIG A. Review of Collision Avoidance and Path Planning Methods for Ships in Close Range Encounters[J]. The Journal of Navigation, 2009, 62(3): 455-476.

[8] MARJANOVIC' M, PRPIC'-ORŠIC' J, TURK A, et al. Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing[J]. Journal of Marine Science and Engineering, 2025, 13(6): 1014-1031.

[9] SHI X, LIANG Y, LEE H, et al. An Improved GA and a Novel PSO-GA-Based Hybrid Algorithm[J]. Information Processing Letters, 2005, 93(5): 255-261.

[10] LONG Y, SU Y, ZHANG H, et al. Application of Improved Genetic Algorithm to Unmanned Surface Vehicle Path Planning; Proceedings of the 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) [C/OL]// 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 2018 [2026-05-15]. https://doi.org/10.1109/DDCLS.2018.8515962.

[11] XIA G, HAN Z, ZHAO B, et al. Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum ant Colony Algorithm [J/OL]. Mathematical Problems in Engineering, 2019, 2019: 2902170 [2026-05-15]. https://doi.org/10.1155/2019/2902170.

[12] SONG C H. Global Path Planning Method for USV System Based on Improved Ant Colony Algorithm[J]. Applied Mechanics and Materials, 2014, 568: 785-788.

[13] KIM H, KIM S-H, JEON M, et al. A Study on Path Optimization Method of an Unmanned Surface Vehicle under Environmental Loads Using Genetic Algorithm[J]. Ocean Engineering, 2017, 142: 616-624.

[14] LAMINI C, BENHLIMA S, ELBEKRI A. Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning[J]. Procedia Computer Science, 2018, 127: 180-189.

[15] ULLAH S, SALAM A, MASOOD M. Analysis and Comparison of a Proposed Mutation Operator and Its Effects on the Performance of Genetic Algorithm[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2022, 25(2): 1208-1216.

[16] ZHANG P, WANG J, TIAN Z, et al. A genetic Algorithm with Jumping Gene and Heuristic Operators for Traveling Salesman Problem [J/OL]. Applied Soft Computing, 2022, 127: 109339 [2026-05-15]. https://doi.org/10.1016/j.asoc.2022.109339.

[17] RUTENBAR R A. Simulated Annealing Algorithms: An Overview[J]. IEEE Circuits and Devices Magazine, 2002, 5(1): 19-26.

基金资助

中国船舶航海保障技术实验室开发基金资助项目(2023010302)

海洋防务技术创新中心创新基金资助项目(JJ-2022-702-01)