LAUSR

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.