The use of unmanned aerial vehicles(UAVs) is becoming more commonplace in search-and-rescue tasks,but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate sea...The use of unmanned aerial vehicles(UAVs) is becoming more commonplace in search-and-rescue tasks,but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive memetic algorithms, and adaptive memetic algorithms on a wide set of problem instances.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 61872123 and 61473263)the Zhejiang Provincial Natural Science Foundation,China (No. LR20F030002)。
文摘The use of unmanned aerial vehicles(UAVs) is becoming more commonplace in search-and-rescue tasks,but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive memetic algorithms, and adaptive memetic algorithms on a wide set of problem instances.
