Ant Colony Optimization Algorithm for Robot Path Planning

DOI

10.2991/eame-15.2015.219How to use a DOI?

Keywords

path planning; ant colony optimization algorithm; robot; Max-Min Ant System

Abstract

In this article two different optimization algorithms are presented to solve the deficiency of ant colony algorithm such as slow convergence rate and easy to fall into local optimum. This method based on Max-Min Ant System, established an adaptive model for pheromone evaporation coefficient adjusted adaptively and avoided the ants falling into local optimum. At the same time, this optimization algorithm used the strategy of the survival of the fittest way to optimize the pheromone update mechanism to accelerate the convergence rate. Finally, by comparison with ant colony algorithm, the simulation results show that, both the optimal path and routing time are optimized, and proved that the optimization algorithm is valid and feasible.

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  - Q.L. Xu
AU  - D.X. Zhang
PY  - 2015/07
DA  - 2015/07
TI  - Ant Colony Optimization Algorithm for Robot Path Planning
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 815
EP  - 817
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.219
DO  - 10.2991/eame-15.2015.219
ID  - Xu2015/07
ER  -