Abstract
We propose in this paper the use fuzzy logic to adjust parameters in thein the particle swarm optimization and ant colony optimization.This paper describe the comparison of the results obtained in the particle swarm optimization (PSO) and the ant colony optimization (ACO) of the resolution of the traveling salesman person (TSP), the adjustment (Xu, 2019 Chinese control and decision conference (CCDC), Nanchang, China, pp 3760–3763 [1]) is performed to improve the behavior of both methods. The particle swarm method and the ant colony methods have parameters, which need to dynamically adjust to improve the performance of both algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
L.A. Zadeh, Fuzzy logic. Computer 21(4), 83–93 (1988)
L.A. Zadeh, Fuzzy Algorithms, vol. 12 (Department of Electrical Engineering and Project MAC, 1968), pp. 94–102
C. Caraveo, F. Valdez, O. Castillo, Optimization mathematical functions for multiple variables using the algorithm of self-defense of the plants, in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2018). Accessed 17 Dec 2019
D. Tian, N. Li, Fuzzy particle swarm optimization algorithm, in 2009 International Joint Conference on Artificial Intelligence (2009). Accessed 17 Dec 2019
M. Dorigo, T. Stützle, Ant colony optimization algorithms for the traveling salesman problem, in Ant Colony Optimization, vol. 547 (A Bradford Book, Tijuana, BC, 2004), pp. 65–119
Z. Zhang, K. Zou, Simple ant colony algorithm for combinatorial optimization problems, in 2017 36th Chinese Control Conference (CCC) (2017). Accessed 20 Jan 2020
Y. Zhang J. Chen, C. Bingham, M. Mahfouf, A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2014). Accessed 15 Jan 2020
A.K. Sharma, V. Singh, N.K. Verma, J. Liu, Condition based monitoring of machine using mamdani fuzzy network, in Prognostics and System Health Management Conference (PHM-Chongqing) (2018), Accessed 15 Jan 2020
Y. Xu, Some new operations on triangular fuzzy number intuitionistic fuzzy set, in Chinese Control And Decision Conference (CCDC) (2019). Accessed 15 Jan 2020
Z.F. Hao, R.C. Cai, H. Huang, An adaptive parameter control strategy for ACO, in 2006 International Conference on Machine Learning and Cybernetics (2006). Accessed 15 Sept 2019
A. Shetty, K.S. Puthusseri, R. Shankaramani, A. Shetty, An improved ant colony optimization algorithm: minion ant (MAnt) and its application on TSP, in 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (2018). https://ieeexplore.ieee.org/document/8628805. Accessed 15 Nov 2019
R.S. Jadon, U. Dutta, Modified ant colony optimization algorithm with uniform mutation using self-adaptive approach for travelling salesman problem, in 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, 2013 (2013). Accessed 16 Dec 2019
M. Dutta, P. Pragya, TSP solution using dimensional ant colony optimization, in 2015 Fifth International Conference on Advanced Computing & Communication Technologies (2015). Accessed 16 Dec 2019
Y. Wang, Improving artificial bee colony and particle swarm optimization to solve TSP problem, in 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) (2018). Accessed 16 Dec 2019
H. Qian, T. Su, Hybrid algorithm based on max and min ant system and particle swarm optimization for solving TSP problem, in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2018). https://ieeexplore.ieee.org/document/8406459. Accessed 16 Dec 2019
S. Akter, S.S. Rahman, M.H. Rahman, M.A. Akhand, Particle swarm optimization with partial search to solve traveling salesman problem, in 2012 International Conference on Computer and Communication Engineering (ICCCE) (2012). https://ieeexplore.ieee.org/document/6271164. Accessed 16 Dec 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Guajardo, H.M., Valdez, F. (2021). Optimization of Routes of a Robot Using Bioinspired Algorithms. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Recent Advances of Hybrid Intelligent Systems Based on Soft Computing. Studies in Computational Intelligence, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-58728-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-58728-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58727-7
Online ISBN: 978-3-030-58728-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)