An Improved Chaotic Self-Adapting Monkey Algorithm for Multi-UAV Task Assignment

Abstract

To solve the task assignment problem of heterogeneous multi-unmanned aerial vehicle (UAV) with different loads, an improved monkey swarm algorithm is proposed. First, the complex combat tasks are divided into three types of subtasks, and the multi-UAV task assignment model is established based on the performance of UAVs with specific loads. Second, an improved chaotic self-adapting monkey algorithm (ICSAMA) is proposed by introducing chaos optimization into the monkey swarm algorithm through the adaptive mechanism. The optimization ability of the improved algorithm is verified by the classical benchmark function containing single/multipeaks. Finally, taking the actual heterogeneous multi-UAV task planning problem as an example, ICSAMA is applied to solve it. The simulation results show that ICSAMA has higher convergence accuracy and robustness than the standard monkey swarm algorithm.