An improved genetic algorithm in path planning for mobile robot

被引：0

作者：

Liu, Guangrui ^{[1
]}

Tian, Xin ^{[1
]}

Zhou, Wenbo ^{[1
]}

Guo, Kefu ^{[1
]}

机构：

[1] Zhengzhou Univ, Sch Mech Engn, Zhengzhou 450001, Henan, Peoples R China

来源：

PROCEEDINGS OF THE 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER, MECHATRONICS, CONTROL AND ELECTRONIC ENGINEERING (ICCMCEE 2015) | 2015年 / 37卷

关键词：

Genetic Algorithm; Path planning; Crossover and mutation; Adaptive adjustment; The elite preservation strategy; Metropolis Guidelines;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper proposed a new way of crossover and mutation for genetic algorithm to prevent the local optima and guarantee the feasibility of the mutated path. Improving the adaptive adjustment of crossover and mutation probability to improve the search efficiency of the algorithm optimization. Contrary to the disadvantages of genetic algorithm, such as it's easy to fall into the optimal local and premature, the Metropolis based on simulated annealing algorithm is used for the optimization of genetic algorithm. By using the improved genetic algorithm to different environment models and comparing with other genetic algorithms, the results show that the use of improved genetic algorithm has better convergence speed and optimization capabilities.

引用

页码：998 / 1003

页数：6