支持强化学习RNSGA-Ⅱ算法在航迹规划中应用

RNSGA-Ⅱ Algorithm Supporting Reinforcement Learning and Its Application in UAV Path Planning

针对传统第二代非支配排序遗传算法(NSGA-Ⅱ)求解无人机多目标三维航迹规划早熟收敛及多样性不足的局限性,提出了支持强化学习RNSGA-Ⅱ算法。设置两个独立种群分别用NSGA-Ⅱ算法独立演化,隔代在两种族之间迁徙,接着各种群进行寻优进化,根据种群多样性的变化运用强化学习算法动态地优化各种群间“迁徙”的比例参数,从而使进化过程保持种群多样性,一定程度上解决了收敛速度和全局收敛性之间的矛盾。仿真结果表明,RNS-GA-Ⅱ算法较单一NSGA-Ⅱ收敛精度更高,解集具有更好的分布性和多样性。

In view of the limitation of traditional NSGA-Ⅱin solving multi-objective UAV 3d path planning,such as the problem of premature convergence and insufficient diversity,an RNSGA-Ⅱalgorithm supporting reinforcement learning is proposed.Two single populations are set to search optimization with the NSGA-Ⅱalgorithm respectively and every certain generation migrate between two populations,then each population performs optimization evolution,according to the change of population diversity using reinforcement learning algorithm to dynamically optimize the proportion of migration among the population of parameters,so that the evolutionary process to keep the population diversity,to some extent,the contradiction between the convergence speed and global convergence is solved.Simulation results show that the RNSGA-Ⅱalgorithm not only has higher convergence accuracy than single NSGA-Ⅱalgorithm but also the solution set has better distribution and diversity.