An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather

被引:7
|
作者
Ma, Yue [1 ]
Li, Bo [2 ]
Huang, Wentao [3 ]
Fan, Qinqin [1 ]
机构
[1] Shanghai Maritime Univ, Logist Res Ctr, Shanghai 201306, Peoples R China
[2] Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China
[3] Shanghai Jiao Tong Univ, Key Lab Control Power Transmiss & Convers, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
maritime search and rescue; path planning; unmanned air vehicle; multi-objective optimization; non-dominated sorting genetic algorithm-II; multi-task optimization; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHM; CONSTRUCTION; ALLOCATION; DESIGN;
D O I
10.3390/jmse11040781
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The international trade heavily relies on maritime transportation. Due to the vastness of the ocean, once an accident happens, fast maritime search and rescue (MSR) is a must, as it is of life-and-death matter. Using unmanned air vehicles (UAVs) is an effective approach to completing complex MSR tasks, especially when the environment is dangerous and changeable. However, how to effectively plan paths for multi-UAVs under severe weather, e.g., to rescue the most urgent targets in the shortest time, is a challenging task. In this study, an improved NSGA-II based on multi-task optimization (INSGA-II-MTO) is proposed to plan paths for multi-UAVs in the MSR tasks. In the INSGA-II-MTO, a novel population initialization method is proposed to improve the diversity of an initial population. Further, two tasks are introduced during the execution of the search algorithm. Namely, one assistant task, which solves a simplified MSR problem through multi-task optimization, is implemented to provide necessary evolutional knowledge to a main task that solves an original MSR problem. The performance of the proposed INSGA-II-MTO is compared with other competitors in three MSR scenarios. Experimental results indicate that the proposed algorithm performs best among the compared ones. It is observed that the INSGA-II-MTO can find a set of shorter total paths and handle the most urgent task in the shortest possible time. Therefore, the proposed method is an effective and promising approach to solving multi-UAVs MSR problems to reduce human causalities and property losses.
引用
收藏
页数:18
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