Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems

Sima Saeed; Aliakbar Niknafs

doi:doi:10.11648/j.ajsea.s.2016050301.16

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Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems

Sima Saeed, Aliakbar Niknafs

Published in American Journal of Software Engineering and Applications (Volume 5, Issue 3-1)

Received: 14 September 2016 Accepted: 23 September 2016 Published: 21 August 2017

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Abstract

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.

Published in	American Journal of Software Engineering and Applications (Volume 5, Issue 3-1) This article belongs to the Special Issue Advances in Computer Science and Information Technology in Developing Countries
DOI	10.11648/j.ajsea.s.2016050301.16
Page(s)	25-29
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Mobile Robot, Fuzzy-Qlearning, Ant Colony Optimization-Fuzzy Q Learning, Bee Colony Optimization-Fuzzy-Q Learning, Artificial Bee Colony-Fuzzy Q Learning

References

[1]	H. R̒. Berenji, “Fuzzy Q-learning for generalization of reinforcement,” IEEE Int. Conf. Fuzzy Syst, 1996.
[2]	P. Y. Glorennec, “Fuzzy Q-learning and dynamic fuzzy Q-learning,” IEEE Int. Conf. Fuzzy Syst., Orlando, 1994.
[3]	P. Y. Glorennec, L. Jouffe, “Fuzzy Q-learning,” IEEE Int. Conf. Fuzzy Syst, 1997.
[4]	L. Jouffe, “Fuzzy, inference system learning by reinforcement methods̕,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., Vol. 28 (3), pp. 338–355, 1998.
[5]	C. F. Juang, “Ant Colony Optimization Incorporated With Fuzzy Q-Learning for Reinforcement Fuzzy Control,” IEEE Transactions on systems, man, and cybernetics—part a: systems and humans, Vol. 39, May 2009.
[6]	L. P. Wong, M. Yoke Hean Low, C. S. Chong, “A Bee Colony Optimization Algorithm for Traveling Salesman Problem,” Second Asia International Conference on Modelling & Simulation, IEEE, 2008.
[7]	L. P. Wong, Y. H. Malcolm Low, C. S. Chong, “Bee Colony Optimization with Local Search for Traveling Salesman Problem,” 2008.
[8]	M. Servet Kiran, H. Iscan, M. Gounduz, “The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem,” Neural Comput and Applic, 2013.
[9]	W. li. Xiang, M. Qing An, “An efficient and robust artificial bee colony algorithm for numerical optimization,” Computers and Operations Research, pp. 1256–1265, 2013.
[10]	S. Saeed, A. Niknafs, “Artificial Bee Colony-Fuzzy Q Learning for Reinforcement Fuzzy Control (Truck Backer-Upper Control Problem),” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 24, No. 1, pp. 123-136, 2016.

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APA Style

Sima Saeed, Aliakbar Niknafs. (2017). Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems. American Journal of Software Engineering and Applications, 5(3-1), 25-29. https://doi.org/10.11648/j.ajsea.s.2016050301.16

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ACS Style

Sima Saeed; Aliakbar Niknafs. Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems. Am. J. Softw. Eng. Appl. 2017, 5(3-1), 25-29. doi: 10.11648/j.ajsea.s.2016050301.16

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AMA Style

Sima Saeed, Aliakbar Niknafs. Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems. Am J Softw Eng Appl. 2017;5(3-1):25-29. doi: 10.11648/j.ajsea.s.2016050301.16

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@article{10.11648/j.ajsea.s.2016050301.16,
  author = {Sima Saeed and Aliakbar Niknafs},
  title = {Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems},
  journal = {American Journal of Software Engineering and Applications},
  volume = {5},
  number = {3-1},
  pages = {25-29},
  doi = {10.11648/j.ajsea.s.2016050301.16},
  url = {https://doi.org/10.11648/j.ajsea.s.2016050301.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.s.2016050301.16},
  abstract = {Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.},
 year = {2017}
}

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TY  - JOUR
T1  - Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems
AU  - Sima Saeed
AU  - Aliakbar Niknafs
Y1  - 2017/08/21
PY  - 2017
N1  - https://doi.org/10.11648/j.ajsea.s.2016050301.16
DO  - 10.11648/j.ajsea.s.2016050301.16
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
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AB  - Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.
VL  - 5
IS  - 3-1
ER  -

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Author Information

Sima Saeed

Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Aliakbar Niknafs

Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

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APA Style

Sima Saeed, Aliakbar Niknafs. (2017). Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems. American Journal of Software Engineering and Applications, 5(3-1), 25-29. https://doi.org/10.11648/j.ajsea.s.2016050301.16

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ACS Style

Sima Saeed; Aliakbar Niknafs. Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems. Am. J. Softw. Eng. Appl. 2017, 5(3-1), 25-29. doi: 10.11648/j.ajsea.s.2016050301.16

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AMA Style

Sima Saeed, Aliakbar Niknafs. Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems. Am J Softw Eng Appl. 2017;5(3-1):25-29. doi: 10.11648/j.ajsea.s.2016050301.16

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@article{10.11648/j.ajsea.s.2016050301.16,
  author = {Sima Saeed and Aliakbar Niknafs},
  title = {Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems},
  journal = {American Journal of Software Engineering and Applications},
  volume = {5},
  number = {3-1},
  pages = {25-29},
  doi = {10.11648/j.ajsea.s.2016050301.16},
  url = {https://doi.org/10.11648/j.ajsea.s.2016050301.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.s.2016050301.16},
  abstract = {Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Optimization Algorithms Incorporated Fuzzy Q-Learning for Solving Mobile Robot Control Problems
AU  - Sima Saeed
AU  - Aliakbar Niknafs
Y1  - 2017/08/21
PY  - 2017
N1  - https://doi.org/10.11648/j.ajsea.s.2016050301.16
DO  - 10.11648/j.ajsea.s.2016050301.16
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 25
EP  - 29
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.s.2016050301.16
AB  - Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.
VL  - 5
IS  - 3-1
ER  -

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