摘要
针对传统蚁群算法在自主式水下机器人的三维路径规划中收敛速度慢、搜索效率低、求解质量差等问题,提出一种基于改进帝国竞争算法的三维路径规划方法。该方法以传统帝国竞争算法为框架,首先,在国家初始化部分引入蚁群思想来搜索初始路径,提高可行解的质量;其次,为确保算法在提高种群多样性的同时不会丢失优秀个体,在殖民地革命部分加入差分进化思想;最后,在3个不同规模的地图上进行仿真比对。仿真结果表明,改进后的帝国竞争算法充分利用传统帝国竞争算法收敛速度快、收敛精度高、具有较强的全局搜索能力的特点,提高了寻优过程的精度,加强了全局的寻优性,有效解决了传统帝国竞争算法容易陷入局部最优解的问题,最优路径的长度缩短了11%。
Aiming at the problems of slow convergence speed, low search efficiency and poor solution quality of traditional ant colony algorithm in three-dimensional path planning of autonomous underwater vehicle, a three-dimensional path planning method based on improved Empire competition algorithm is proposed. This method takes the traditional imperial competition algorithm as the framework. Firstly, the ant colony idea is introduced into the national initialization part to search the initial path and improve the quality of feasible solutions;Secondly, in order to ensure that the algorithm will not lose excellent individuals while improving population diversity, the idea of differential evolution is added to the part of colonial revolution;Finally, the simulation comparison is carried out on three maps of different scales. The simulation results show that the improved imperial competition algorithm makes full use of the characteristics of fast convergence speed, high convergence accuracy and strong global search ability of the traditional imperial competition algorithm, improves the accuracy of the optimization process, strengthens the global optimization, effectively solves the problem that the traditional imperial competition algorithm is easy to fall into the local optimal solution, and the length of the optimal path is shortened by 11%.
作者
尹姝呓
毛剑琳
李斌
Yin Shuyi;Mao Jianlin;Li Bin(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2022年第10期74-81,共8页
Electronic Measurement Technology
基金
云南省重点研发计划项目(202002AC080001)资助
关键词
水下机器人
三维路径规划
帝国竞争算法
差分进化算法
autonomous underwater vehicle
three-dimensional path planning
imperial competition algorithm
differential evolution algorithm
作者简介
尹姝呓,硕士研究生,主要研究领域为水下机器人的三维路经规划。E-mail:1737067589@qq.com;毛剑琳,教授,博士生导师,主要研究领域为通信网络资源分配、网络化控制系统研究、智能优化算法研究等。E-mail:1318524654@qq.com;李斌,硕士研究生,主要研究领域为水下机器人的三维路经规划。
