基于改进 ABC 算法的有-无人协同空战行为建模

To solve the problem of difficulty in establishing collaborative behavior models and weak adversarial capabilities in typical MAV/UAV air combat scenarios, a mixed decision based MAV/UAV behavior modeling framework is proposed. Using collaborative rule sets, rule subsets, tactical action sets, and other tools, a hierarchical decision collaborative behavior model supporting five types of collaborative tactics, including grinding tactics and unilateral flanking tactics, is constructed in this framework. a behavior model parameter optimization method based on an improved artificial bee colony (ABC) algorithm is proposed. By using the Mason rotation method to initialize the population, a better initial honey source was obtained, and the exploration strategy during the reconnaissance bee stage was improved. The results indicate that the collaborative behavior model has strong adversarial capabilities when simulated and validated in typical aerial combat scenarios. By optimizing the parameters of the tactical action set using the improved ABC algorithm, the efficiency of behavior model construction and adversarial effects can be improved.