Multi-UAV Cooperative Task Allocation Based on Improved Spider Monkey Algorithm
Guoqing Shi, Wei Pang, Longmeng Ji, Jiandong Zhang, Qiming Yang, Yaozhong Zhang, Xiang Yan · 2024
This paper investigates the problem of cooperative ground task allocation for multiple UAVs, which consists of two parts: pre-allocation and dynamic allocation. To address the pre-allocation problem, a dynamic adaptive inertia weight chaotic discrete spider monkey algorithm (WCDSMO) is proposed by incorporating dynamic adaptive inertia weight and chaotic strategy into the spider monkey optimization (SMO), thereby enhancing the algorithm’s global search capability and population diversity. For dynamic task allocation, an improved contract law algorithm (CNP) is introduced based on the WCDSMO algorithm to meet real-time requirements and improve efficiency in allocating cooperative tasks for multiple UAVs. A WCDSMO algorithm model that includes the improved CNP algorithm is constructed. The enhanced contract network algorithm is employed in generating initial solutions and coordinating illegal solution tasks, resulting in improved efficiency of the overall algorithm. Finally, simulation examples are conducted to validate the effectiveness of this multi-UAV cooperative ground attack task allocation algorithm.