Performance Enhancement of Adaptive Neuro-Fuzzy Inference System through Population-Based Algorithms
DOI:
https://doi.org/10.22399/ijcesen.4145Keywords:
ANFIS, Metaheuristic Optimization, arameter Tuning, Fuzzy Logic SystemsAbstract
The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a powerful hybrid artificial intelligence model that combines the learning capabilities of neural networks with the reasoning of fuzzy logic. It is widely used to model complex relationships between input and output parameters across various domains. However, the performance of ANFIS is highly dependent on the optimal setting of its internal parameters, making their optimization a significant research focus. This work aims to enhance ANFIS performance by optimizing its parameters using seven well-recognized metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). The proposed hybrid models were evaluated on three different datasets. Experimental results demonstrate that the hybrid models, which integrate these optimization algorithms with ANFIS, achieve a significant performance improvement compared to the standard ANFIS model.
References
[1] Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation,2(4).https://doi.org/10.1016/j.xinn.2021.100179
[2]Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
[3]Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: an overview. In Journal of physics: conference series (Vol. 1142, p. 012012). IOP Publishing. https://doi.org/10.1088/1742-6596/1142/1/012012
[4]Pickering, L., Cohen, K., & De Baets, B. (2025). A Narrative Review on the Interpretability of Fuzzy Rule-Based Models from a Modern Interpretable Machine Learning Perspective. International Journal of Fuzzy Systems, 1-20. https://doi.org/10.1007/s40815-025-02022-z
[5]Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541
[6]Talpur, N., Salleh, M.N.M., Hussain, K., Ali, H. (2019). Modified ANFIS with Less Model Complexity for Classification Problems. In: Omar, S., Haji Suhaili, W., Phon-Amnuaisuk, S. (eds) Computational Intelligence in Information Systems. CIIS 2018. Advances in Intelligent Systems and Computing, vol 888. Springer, Cham. https://doi.org/10.1007/978-3-030-03302-6_4
[7]Karaboga, D., & Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4), 2263-2293. https://doi.org/10.1007/s10462-017-9610-2
[8]Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541
[9]Shihabudheen, K. V., & Pillai, G. N. (2018). Recent advances in neuro-fuzzy system: A survey. Knowledge-Based Systems, 152, 136-162. https://doi.org/10.1016/j.knosys.2018.04.014
[10]Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), 116-132. https://doi.org/10.1109/TSMC.1985.6313399
[11]Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35(3), 268-308. https://doi.org/10.1145/937503.937505
[12]Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information sciences, 237, 82-117. https://doi.org/10.1016/j.ins.2013.02.041
[13]Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In: Proceedings of ICNN’95- International Conference on Neural Networks, 4, 1942–1948. http://dx.doi.org/10.1109/ICNN.1995.488968
[14]Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.
[15]Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
[16]Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191. https://doi.org/10.1016/j.advengsoft.2017.07.002
[17]Seyyedabbasi, A., & Kiani, F. (2023). Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with computers, 39(4), 2627-2651. https://doi.org/10.1007/s00366-022-01604-x
[18]Mohammed, H., & Rashid, T. (2023). FOX: a FOX-inspired optimization algorithm. Applied Intelligence, 53(1), 1030-1050. https://doi.org/10.1007/s10489-022-03533-0
[19]El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Abdelhamid, A. A., Eid, M. M., & Ibrahim, A. (2024). Greylag goose optimization: nature-inspired optimization algorithm. Expert Systems with Applications, 238, 122147. https://doi.org/10.1016/j.eswa.2023.122147
[20]Timrie. (2023). Banana [Dataset]. Kaggle. Retrieved October 20, 2025, from https://www.kaggle.com/datasets/timrie/banana
[21]Yeh, I-C. (2008). Blood Transfusion Service Center [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5GS39
[22]Lohweg, V. (2012). Banknote Authentication [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C55P57
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