复杂环境下改进AGNES多智能体动态联盟算法

Improved AGNES Multi-agent Dynamic Alliance Algorithm in Complex Environment

在当前战场环境反馈输入所占比重日益增大的情况下,提出利用无监督学习中的AGNES层次聚类算法对传统多源智能体联盟理论框架进行改进。综合考虑环境和传感器不同个体效能中的复杂性与模糊性因素,对联盟的多智能体模型进行描述以及多智能体交叉提示下动态联盟探测系统具体提示步骤进行设计,针对性改进无监督学习AGNES聚类算法结合目标探测信息的认知度一致性函数,使系统直接从变化的环境中建立动态模型,从而在达到联盟优化任务的长期收益累计的同时对目前的短期收益做出更有利的决策。仿真表明,相对于群智能算法等传统整体调动策略,改进算法是更加符合实际需求的智能化方法。

With the increasing proportion of feedback input in battlefield environment,AGNES hierarchical clustering algorithm in unsupervised learning is proposed to improve the traditional theoretical framework of multi-agent alliance.Considering the complexity and fuzziness factors in the environment and the efficiency of different individual sensors,the multi-agent model of the alliance is described and the specific prompting steps of the dynamic alliance detection system under the multi-agent cross prompt are designed.Furthermore,the cognition consistency function combined the unsupervised learning AGNES clustering algorithm with target detection information is improved,so that the system is able to directly establish a dynamic model from the changing environment,thus to achieve the long-term benefit accumulation of the alliance optimization task and make a more favorable decision for the current short-term benefit.Simulation shows that compared with the traditional overall mobilization strategy such as swarm intelligence algorithm,the improved algorithm is more in line with practical needs.