面向隐式信息场的动态均衡寻源方法

A dynamic balance source seeking method for implicit information field

受信息分布特征和测量技术的限制,隐式信息场具有显著的观测局限性,通常观测者无法获得未到达区域的数据,从而导致寻源行为具有强烈的不确定性。受生物寻源行为的启发,文中从搜索行为与信息获取和利用的角度出发,提出了一种基于动态均衡策略的自主寻源方法。首先,借助多目标优化理论,建立寻源数学模型,将隐式信息场寻源任务归结为多目标收敛问题;其次,借鉴进化算法的思想,将运动搜索与种群进化相结合,构建以可行性搜索行为为个体样本的进化种群,通过对多目标问题的求解,引导载体完成寻源任务;然后,在寻源过程中,引入分布熵度量搜索偏向,结合寻源进程的需求,设计了一种动态均衡搜索的寻源方法,并给出寻源算法;最后,通过理论分析和仿真验证,证实了所提方法的有效性和合理性。

The implicit information field has significant observation boundedness due to the limitation of the information distribution characteristics and measurement techniques.The observer usually cannot obtain the data of the unreached region,which leads to the strong uncertainty of the source⁃seeking behavior.With the inspiration of biological homing behavior,an autonomous source searching method based on a dynamic balanced strategy is presented in this paper according to the relationship of searching behavior with information acquisition and utilization.By means of multi⁃objetive optimization theory,the source searching mathematic model is built,and the source seeking task in implicit information field is summed up as a multi⁃objective convergence.The evolutionary population taking the feasibility search behavior as the individual sampling is established by drawing on the idea of evolutionary algorithms and combining motion search with population evolution.The carrier is guided to complete source seeking task by solving the multi⁃objective problem.The distribution entropy is introduced to measure the search bias in the process of source seeking.In combination with the demand of the source seeking process,a source seeking method of dynamic balanced search method is designed,and the source seeking algorithm is given.The effectiveness and rationality of the proposed method have been proved by theoretical analysis and simulation verification.