基于差分进化算法的改进遗传算法的UUV路径规划
Improved Genetic Algorithm for UUV Path Planning Based on Differential Evolution Algorithm
赵鹏 1丁雪 1程婷婷 1莫小艳1
作者信息
- 1. 哈尔滨工程大学智能科学与工程学院,黑龙江哈尔滨 150001
- 折叠
摘要
针对水下无人航行器在三维环境中的全局路径规划问题,从优化初始种群和提高收敛精确度寻得最优路径的角度改进遗传算法,并对遗传算法和差分进化算法的融合进行了研究.采用精英反向学习的方式筛选初始种群,寻得较优初始种群并融合差分进化算法思想改进遗传算法,提升了算法的全局搜索能力.结果表明,改进算法的初期收敛速度较快,规划的曲线能够降低UUV能耗,在一定程度上改善了陷入局部最优解的情况.
Abstract
Aiming at the global path planning problem of underwater unmanned vehicles in a three-dimensional environment,the genetic algorithm was improved from the perspective of optimizing the initial population and improving convergence accuracy to find the optimal path.The fusion of genetic algorithm and differential evolution algorithm was studied.Using elite reverse learning to screen the initial population,finding the optimal initial population and integrating the idea of differential evolution algorithm to improve the genetic algorithm,enhancing the algorithm's global search ability.The results show that the improved algorithm has a faster initial convergence speed,and the planned curve can reduce UUV energy consumption,which to some extent improves the situation of getting stuck in local optimal solutions.
关键词
遗传算法/差分进化算法/全局路径规划Key words
genetic algorithm/differential evolution algorithm/global path planning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据