[1]谢玉龙,王直.基于改进遗传算法的船舶路径规划[J].计算机技术与发展,2019,29(05):152-156.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 032]
　XIE Yu-long,WANG Zhi.Path Planning for Ship Based on Improved Genetic Algorithm[J].,2019,29(05):152-156.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 032]
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基于改进遗传算法的船舶路径规划()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年05期
页码:
152-156
栏目:
应用开发研究
出版日期:
2019-05-10

文章信息/Info

Title:
Path Planning for Ship Based on Improved Genetic Algorithm
文章编号:
1673-629X(2019)05-0152-05
作者:
谢玉龙; 王直
江苏科技大学 电子信息学院,江苏 镇江 212003
Author(s):
XIE Yu-long; WANG Zhi
School of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang 212003,China
关键词:
改进遗传算法; 路径规划; 遗传操作; 仿真实验
Keywords:
improved genetic algorithm; route planning; genetic operation; simulation
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 05. 032
摘要:
针对传统遗传算法解决船舶路径规划问题的不足,提出了一种改进的遗传算法。 改进算法改变了种群的编码方式,由二维编码变为基于坐标轴的一维编码;在传统遗传算法的基础上增加三种新的遗传操作:复原操作、重构操作和录优操作;复原、重构操作能够避免算法收敛于局部最优解,使算法尽早收敛于全局最优解,录优操作保证种群朝着最优解方向进化。 另外设计了插入算子、删除算子和平滑算子来提高种群进化效率和生成路径的现实意义。 计算机仿真结果表明,在不同的航海环境中,该算法能够找到平滑的全局最优路径,验证了算法的可行性、有效性和现实性。 另外,该算法生成路径的长度和运行时间相比于传统算法均有所提高。
Abstract:
Aiming at the shortcomings of traditional genetic algorithm to solve ship path planning problems,we propose an improved genetic algorithm. This algorithm changes the coding mode of the population from two-dimensional coding to one-dimensional coding based on coordinates axes,and adds three new operations:restoration,reconstruction and recording. The restoration and reconstruction can avoid the convergence of the algorithm to the local optimal solution and makes it converge to the global optimal solution as soon as possible,and the recording ensures that the population evolves toward the direction of the optimal solution. The insertion,deletions and smoothing operators are designed to improve the evolutionary efficiency of populations and their practical significance. Finally, the computer simulation shows that the algorithm can find a smooth global optimal path in different navigation environments,which verifies its feasibility,validity and reality. In addition,the path length and run time of the improved algorithm generation are better than traditional algorithms.

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更新日期/Last Update: 2019-05-10