基于Maklink图和布谷鸟搜索算法的施工水域路径规划  

Path planning in construction waters based on Maklink graph and cuckoo search algorithm

作　　者：张波菲 谢新连[1] 何傲 ZHANG Bofei;XIE Xinlian;HE Ao(Logistics Research Institute, Dalian Maritime University, Dalian 116026, Liaoning, China)

机构地区：[1]大连海事大学物流研究院,辽宁大连116026

出　　处：《上海海事大学学报》2020年第3期6-11,30,共7页Journal of Shanghai Maritime University

基　　金：国家重点研发计划(2017YFC0805309);中央高校基本科研业务费专项资金(3132016358)。

摘　　要：为提高船舶在复杂施工水域通行的安全性,提出一种基于Maklink图和布谷鸟搜索(cuckoo search,CS)算法的船舶路径规划方法。利用改进的Maklink图构建施工水域环境模型;设置变量参数并用改进的CS算法对模型进行求解,其中采用基于Dijkstra算法得到的最短路径长度作为种群个体的适应度值;采用3个衡量算法性能的指标——优化性能指标、时间性能指标和动态性能指标,对多种算法进行分析比较。结果表明,采用指数型自适应步长和线性自适应发现概率对CS算法进行改进,能提高其在路径规划中的搜索效率和迭代速度,并可以保证求出一定精度内的近似最优解,显示出该算法的优越性。In order to improve the navigation safety of ships in complex construction waters,a ship path planning method based on the Maklink graph and the cuckoo search(CS)algorithm is proposed.The construction waters environment model is constructed by the improved Maklink graph;the variable parameters are set and the model is solved by the improved CS algorithm.The shortest path length calculated by the Dijkstra algorithm is used as the fitness value of the population individual.Three performance indexes for measuring the algorithm performance are used to analyze and compare many algorithms,where three performance indexes are the optimization performance index,the time performance index and the dynamic performance index,respectively.The results show that the improvement of the CS algorithm by the exponential adaptive step size and the linear adaptive discovery probability can improve its search efficiency and iteration speed in path planning,and can guarantee to obtain the approximate optimal solution within a certain precision,which shows the superiority of the algorithm.

关 键 词：船舶避障 智能交通 布谷鸟搜索(CS)算法 性能指标 施工水域 Maklink图 

分 类 号：U675.73[交通运输工程—船舶及航道工程]