Multi-UAV Reconnaissance Task Allocation Based on an Improved Wolf Pack Algorithm

In the task allocation problem for multiUAV(Multiple Unmanned Aerial Vehicles) cooperative reconnaissance and strike missions, insufficient solution accuracy often leads to redundant mission paths and reduced strike effectiveness. This paper constructs an optimized task allocation model tailored for complex battlefield conditions. To enhance computational efficiency, a Genetic-Wolf Pack Algorithm (GWPA-PT) integrated with genetic strategies is proposed. This approach discretizes the wandering, summoning, and sieging behaviors of the wolf pack algorithm while introducing an individual replenishment mechanism to enhance its applicability to discrete task allocation scenarios. Additionally, an elite experience learning mechanism was designed to guide population reconstruction using the superior allocation experience of elite groups, thereby significantly enhancing iteration efficiency. Comparative experiments demonstrate that compared to typical multi-UAV task allocation algorithms in recent years, the GWPA-PT algorithm exhibits outstanding accuracy in solving problems, providing an efficient and reliable solution for collaborative task allocation in complex environments.