关键词: 双种群, 迁徙, NSGA-II, 航迹规划, 强化学习

Abstract: In view of the limitation of traditional NSGA-II in solving multi-objective UAV 3d path planning, such as the problem of premature convergence and insufficient diversity, an RNSGA-II algorithm supporting reinforcement learning is proposed. Two single populations are set to search optimization with the NSGA-II algorithm 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-II algorithm not only has higher convergence accuracy than single NSGA-II algorithm but also the solution set has better distribution and diversity.

Key words: double populations, migration, NSGA-II, path planning, reinforcement learning