Due to their advantages in flexibility,scalability,survivability,and cost-effectiveness,drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern ba...Due to their advantages in flexibility,scalability,survivability,and cost-effectiveness,drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields.This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms.Given a set of available air defense systems,the problem determines the location of each air defense system in a predetermined region,such that the cost for enemy drones to pass through the region would be maximized.The cost is calculated based on a counterpart drone path planning problem.To solve this adversarial problem,we first propose an exact iterative search algorithm for small-size problem instances,and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances.We implement the evolutionary framework with six popular evolutionary algorithms.Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.展开更多
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supported by the National Natural Science Foundation of China(No.61872123)the Natural Science Foundation of Zhejiang Province(No.LR20F030002).
文摘Due to their advantages in flexibility,scalability,survivability,and cost-effectiveness,drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields.This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms.Given a set of available air defense systems,the problem determines the location of each air defense system in a predetermined region,such that the cost for enemy drones to pass through the region would be maximized.The cost is calculated based on a counterpart drone path planning problem.To solve this adversarial problem,we first propose an exact iterative search algorithm for small-size problem instances,and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances.We implement the evolutionary framework with six popular evolutionary algorithms.Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.
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.
