Multi-Type Task Assignment Algorithm for Heterogeneous UAV Cluster Based on Improved NSGA-Ⅱ

Abstract

Cluster warfare, as a disruptive technology, leverages its numerical advantage to overcome limitations such as restricted task execution types and the low resilience of single platforms, embodying a significant trend in future unmanned combat. In scenarios where only the number of known targets and their vague locations within the region are available, UAV clusters are tasked with performing missions including close-range scout, target attack, and damage assessment for each target. Consequently, taking into account constraints such as assignment, payload, task time window, task sequencing, and range, a multi-objective optimization model for task assignment was formulated. Initially, optimization objectives were set as total mission completion time, total mission revenue, and cluster damage level. Subsequently, the concept of constraint tolerance was introduced to enhance the non-dominant sorting mechanism of NSGA-II by distinguishing individuals that fail to meet constraints, thereby enabling those violating constraints with high tolerance to be retained in the next generation to participate in further evolution, thereby resolving the difficulty of achieving a convergent Pareto solution set under complex interdependent task constraints. Finally, through comparisons, the superiority of the improved NSGA-II algorithm has been verified.