Abstract

This article delves into the realm of collaborative mission planning involving both manned and unmanned systems, leveraging the Artificial Bee Colony (ABC) Algorithm. In the context of current challenges in intelligent decision-making, especially the effective coordination of manned and unmanned entities in complex systems, this research focuses on the principles and evolution of the ABC algorithm and its role in intelligent decision-making scenarios. The study begins with a detailed analysis of the ABC algorithm's basic principles, examining its potential to facilitate collaborative planning between manned and unmanned systems. This examination leads to the construction of a specialized mission planning model tailored to the unique dynamics of such collaborations. The paper then progresses to an optimization of the ABC algorithm, aiming to heighten the efficiency and effectiveness of task planning. Anticipating a breakthrough in task planning methodologies, the study proposes a system capable of achieving optimized resource allocation and heightened decision-making efficiency in environments that involve both manned and unmanned elements. A key highlight of this research is the comprehensive analysis of the ABC algorithm's applicability in complex systems. This serves as the foundation for introducing an innovative solution, addressing the nuanced challenges faced in contemporary intelligent decision-making. This innovative approach promises to advance the field by offering new perspectives and methodologies in orchestrating collaborative missions.