摘要
文中针对传统蚁群算法收敛速度慢以及传统人工势场法目标不可达、易陷入局部最小值等问题,提出了以势场合力为启发信息的改进蚁群算法.该算法运用势场合力结合船舶与目标点间距离信息构建综合启发信息,使蚁群对障碍物具有预避障能力;引入最差蚁群影响,对修改信息素更新机制,提高蚁群搜寻路径的目的性,并对信息素挥发系数进行自适应调节,且对其设定阈值,提升算法的全局搜寻性.通过仿真实验,该算法在实时性以及路径长度上较传统算法有显著提升.
Aiming at the problems of slow convergence speed of traditional ant colony algorithm,unattainable target and easy to fall into local minimum in traditional artificial potential field method,an improved ant colony algorithm with potential field force as heuristic information was proposed.The algorithm adopted potential force and distance information between ship and target point to construct comprehensive heuristic information,which made ant colony have the ability to avoid obstacles in advance.The worst ant colony influence was introduced to modify the pheromone update mechanism,improved the purpose of ant colony search path,and adaptively adjusted the pheromone volatilization coefficient.By setting the threshold,the global searching ability of the algorithm could be further improved.Through simulation experiments,the real-time performance and path length of this algorithm are significantly improved compared with traditional algorithms.
作者
龚铭凡
徐海祥
冯辉
薛学华
GONG Mingfan;XU Haixiang;FENG Hui;XUE Xuehua(Key Laboratory of High Performance Ship Technology Ministry of Education,Wuhan 430063,China;School of Transportation,Wuhan University of Technology,Wuhan 430063,China)
出处
《武汉理工大学学报（交通科学与工程版）》
2020年第6期1072-1076,共5页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
国家自然科学基金项目(51879210,51979210)
中央高校基本科研业务费专项资金项目(2019III040,2019III132CG)资助。
关键词
智能船舶
路径规划
人工势场
蚁群算法
启发信息函数
intelligent ship
path planning
artificial potential field
ant colony algorithm
heuristic information function
