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.

Multi-robot task allocation (MRTA) is a challenging bi-level problem in the multi-robot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are (1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments; and (2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved multimodal multi-objective evolutionary algorithm (MMOEA). The travelling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multi-objective bi-level optimization problem while saving computational costs.