摘要
本文针对已有的以曼哈顿距离作为适应度函数而得出的非最佳路径,通过采用以起点到终点间的直线距离作为适应度函数的遗传算法优化,将其优化为效率更高、更为优秀的路径。经实验验证,证实了此方法在普通二维地图路径优化问题中的优越性。
Aiming at the existing non-optimal path obtained by taking the Manhattan distance as the fitness function,the genetic algorithm is optimized by using the straight line distance between the starting point and the end point as the fitness function.it is optimized into a more efficient and better path,and the superiority of this method in the ordinary two-dimensional map path optimization problem is proved by experiments.
作者
张耀允
谭振江
周伟
方大甲
ZHANG Yaoyun;TAN Zhenjiang;ZHOU Wei;FANG Dajia(School of computer Science,Jilin normal University,Siping Jilin 136000,China;KeyLaboratory of numerical simulation,Jilin Normal University,Siping Jilin 136000,China;Affiliated institution of Jilin normal University,Siping Jilin 136000,China)
出处
《智能计算机与应用》
2021年第6期153-156,共4页
Intelligent Computer and Applications
基金
教育部科技司赛尔网络下一代互联网技术创新项目(NGII20180408)
吉林省教育厅项目(JJKH20200441SK)
吉林省教育教学改革研究课题(JLJJ719920190723194557,2017ZCZ045)
吉林师范大学教学成果培育项目(“三基一新”型计算机专业人才培养模式研究与实践)。
关键词
适应度函数
寻路
路径优化
遗传算法
fitness function
path finding
path optimization
genetic algorithm
