An improved genetic algorithm in path planning for mobile robot

DOI

10.2991/iccmcee-15.2015.186How to use a DOI?

Keywords

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

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guangrui Liu
AU  - Xin Tian
AU  - Wenbo Zhou
AU  - Kefu Guo
PY  - 2015/11
DA  - 2015/11
TI  - An improved genetic algorithm in path planning for mobile robot
BT  - Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 991
EP  - 996
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccmcee-15.2015.186
DO  - 10.2991/iccmcee-15.2015.186
ID  - Liu2015/11
ER  -