摘要
优化选择一定的行动策略能促使任务联盟向期望的目标效果演化。考虑部分事件/行动在不同时段下影响强度不相一致,使用考虑影响值时变的动态影响网对联盟演化过程行动策略优选问题进行建模,给出因果强度逻辑下概率传播参数设计的一致性条件,并基于因果强度逻辑进行影响值计算。基于物种进化中存在基因漂流的特性,设计一种学习型遗传算法(learnable genetic algorithm,LGA)对行动策略优选模型进行优化求解,通过染色体种群对优秀染色体优势基因位学习,结合有效的遗传和选择算子,加快算法收敛寻优速度。结合空中进攻作战想定案例进行仿真验证,计算结果表明,在部分事件/行动节点影响值变化下进行策略优选,提高了对因果关系的建模能力,所提的学习型遗传算法具有良好的收敛性和较好的寻优能力。
Select an optimized action policy can make task coalition evolve into the expected operation effect. Considering the influence strength of events is not consistent in different time horizon,timed varied dynamic in-fluence nets are utilized to model optimized action policy selection problem in the process of task coalition evolu-tion.The consistent conditions of probability propagation parameter design are given and the influence constant is computed,both of which are based on causal strength logic.An learnable genetic algorithm （LGA）based on the gene floating theory is designed to solve the optimized action policy selection model.In LGA,in order to en-hance the algorithm convergence rate,the whole chromosomes learn the superiority gene bit from the first-rank chromosome and combine effective genetic and selecting operators.At last,the simulated results of aerial attack campaign show that optimized action policy selection with various influence constants can improve the capability of cause and effect modeling,and the learnable genetic algorithm good fine convergent and optimizing capability.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2014年第8期1527-1536,共10页
Systems Engineering and Electronics
基金
国家自然科学基金(61273048)
空军工程大学信息与导航学院博士创新基金(KDY2011-002)资助课题
关键词
联盟演化
行动策略
动态影响网
学习型遗传算法
因果强度逻辑
coalition evolution
action policy
dynamic influence nets
learnable genetic algorithm
causal strength logic
