马凯强¹, 任利锋², 曾喆¹, 万剑华¹
1.中国石油大学(华东) 海洋与空间信息学院, 山东 青岛 266580;2.中国石油集团东方地球物理勘探有限责任公司海洋物探分公司, 天津 300457

摘要:
为了保障救援船舶在恶劣海况环境下能够安全、高效地到达救援区域，本文改进人工鱼群算法开展海上救援路径规划研究。选取地形、海风和海浪等指标进行风险度量，采用GIS（geographical information system）多指标决策方法构建海洋环境威胁场，通过粒子群算法计算救援路径规划的最佳参数，并实现海上救援路径搜索。实验表明，基于改进的人工鱼群算法规划的船舶救援路径效果更优，可为海上应急救援提供辅助决策参考。
关键词:恶劣海况路径规划人工鱼群算法
DOI：10.11759/hykx20201108007
分类号:K928.44;U676.8
基金项目:国家重点研发计划项目（2017YFC1405600）；国家自然科学基金项目（41976184）

Ship rescue path planning considering adverse sea conditions
MA Kai-qiang¹, REN Li-feng², ZENG Zhe¹, WAN Jian-hua¹
1.College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China;2.BGP INC, China National Petroleum Corporation, Tianjin 300457, China
Abstract:
To ensure that the rescue ships can reach the rescue area safely and efficiently under adverse sea conditions, this study aims to improve the artificial fish swarm algorithm for maritime rescue path planning. The terrain, sea breeze, and sea wave are selected to measure the risk. The multi-index decision-making method of the geographic information system is used to construct the marine environment threat field. The optimal parameters of rescue path planning are calculated using the particle swarm optimization algorithm, and maritime rescue path planning is realized. The experimental results show that ship rescue path planning based on the improved artificial fish swarm algorithm is effective and can provide auxiliary decision-making reference for maritime emergency rescue.
Key words:bad seapath planningartificial fish swarm algorithm





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