Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2024, Cilt: 7 Sayı: 1, 112 - 126, 30.04.2024

Aref Yelghi

https://doi.org/10.35377/saucis...1414742

Öz

Kaynakça

  • [1] Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
  • [2] Kar S, Das S, Ghosh PK (2014) Applications of neuro-fuzzy systems: a brief review and future outline. Appl Soft Comput 15:243–259
  • [3] Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353.
  • [4] MOPSO based TCSC–ANFIS–POD technique: Design, simultaneous scheme, power system oscillations suppression AD Falehi - Journal of Intelligent & Fuzzy Systems, 2018.
  • [5] M. Taheri, M.R. Alavi Moghaddam, M. Arami, Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models, Journal of Environmental Management, Volume 128, 2013, Pages 798-806, ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2013.06.029.
  • [6] Karaboga, D., & Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4), 2263-2293.
  • [7] Haznedar, B., & Kalinli, A. (2018). Training ANFIS structure using simulated annealing algorithm for dynamic systems identification. Neurocomputing, 302, 66-74.
  • [8] Marzi, H., Haj Darwish, A., & Helfawi, H. (2017). Training ANFIS using the enhanced Bees Algorithm and least squares estimation. Intelligent Automation & Soft Computing, 23(2), 227-234.
  • [9] Pannu, H. S., Singh, D., & Malhi, A. K. (2019). Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring. Neural computing and applications, 1-11.
  • [10] A Jamali, H Babaei, N Nariman-Zadeh, SH Ashraf Talesh and T Mirzababaie Mostofi (2020). Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications. Vol 234, Issue 3, 2020.368-378 https://doi.org/10.1177/1464420716660332
  • [11] V. Seydi Ghomsheh, M. Aliyari Shoorehdeli, A. Sharifi and M. Teshnehlab, "Multi objective optimization of ANFIS structure," 2007 International Conference on Intelligent and Advanced Systems, Kuala Lumpur, 2007, pp. 249-253, doi: 10.1109/ICIAS.2007.4658384.
  • [12] Carrano, E. G., Takahashi, R. H., Caminhas, W. M., & Neto, O. M. (2008, June). A genetic algorithm for multiobjective training of ANFIS fuzzy networks. In 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence) (pp. 3259-3265). IEEE.
  • [13] Azimi, H., Bonakdari, H., Ebtehaj, I., Talesh, S. H. A., Michelson, D. G., & Jamali, A. (2017). Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets and Systems, 319, 50-69.
  • [14] Qi Liu, Xiaofeng Li, Haitao Liu, Zhaoxia Guo (2020), Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art, Applied Soft Computing,v(93), 1568-4946, 106382
  • [15] Kaisa Miettinen (1999). Nonlinear Multiobjective Optimization. Springer. ISBN 978-0-7923-8278-2. Retrieved 29 May 2012.
  • [16] Jump up to:a b c d e f Ching-Lai Hwang; Abu Syed Md Masud (1979). Multiple objective decision making, methods and applications: a state-of-the-art survey. Springer-Verlag. ISBN 978-0-387-09111-2. Retrieved 29 May 2012.
  • [17] B. Xu, S. Li, A. A. Razzaqi, L. Wang and M. Jiao, "A Novel ANFIS-AQPSO-GA-Based Online Correction Measurement Method for Cooperative Localization," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-17, 2022, Art no. 9504417, doi: 10.1109/TIM.2022.3156997.
  • [18] A. Lefteh, M. Houshmand, M. Khorrampanah and G. F. Smaisim, "Optimization of Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) with Artificial Bee Colony (ABC) Algorithm for Classification of Bone Cancer," 2022 Second International Conference on Distributed Computing and High Performance Computing (DCHPC), 2022, pp. 78-81, doi: 10.1109/DCHPC55044.2022.9731840.
  • [19] N. Srinivas, K. Deb, Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms, Evol Comput, 2 (1994) 221-248.
  • [20] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, Ieee T Evolut Comput, 6 (2002) 182-197.
  • [21] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, in, ETH Zurich, 2001, pp. 21.
  • [22] C.A.C. Coello, G.T. Pulido, M.S. Lechuga, Handling multiple objectives with particle swarm optimization, Ieee T Evolut Comput, 8 (2004) 256-279.
  • [23] R. Hedayatzadeh, B. Hasanizadeh, R. Akbari, K. Ziarati, A multi-objective Artificial Bee Colony for optimizing multi-objective problems, in: 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 2010, pp. V5-277-V275-281.
  • [24] Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert systems with applications, 47, 106-119.
  • [25] Sharma, P., & Sahoo, B. B. (2022). An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine. International Journal of Hydrogen Energy, 47(44), 19298-19318.
  • [26] Nwobi-Okoye, C. C., Ochieze, B. Q., & Okiy, S. (2019). Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite. Journal of Materials Research and Technology, 8(3), 3054-3075.
  • [27] Nguyen, M. D., Nguyen, D. D., Hai, H. N., Sy, A. H., Quang, P. N., Thai, L. N., ... & Pham, B. T. (2023). Estimation of Recompression Coefficient of Soil Using a Hybrid ANFIS-PSO Machine Learning Model. Journal of Engineering Research.
  • [28] Li, J., Yan, G., Abbud, L. H., Alkhalifah, T., Alturise, F., Khadimallah, M. A., & Marzouki, R. (2023). Predicting the shear strength of concrete beam through ANFIS-GA–PSO hybrid modeling. Advances in Engineering Software, 181, 103475.
  • [29] Devaraj, R., Mahalingam, S. K., Esakki, B., Astarita, A., & Mirjalili, S. (2022). A hybrid GA-ANFIS and F-Race tuned harmony search algorithm for Multi-Response optimization of Non-Traditional Machining process. Expert Systems with Applications, 199, 116965.

Estimation single output with a hybrid of ANFIS and MOPSO_HS

Yıl 2024, Cilt: 7 Sayı: 1, 112 - 126, 30.04.2024

Aref Yelghi

https://doi.org/10.35377/saucis...1414742

Öz

In the field of soft computing, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been more well-liked in recent years for its predictive capabilities. Appropriate ANFIS parameter adjusting is critical, which creates a gap in its predictive integration with traditional optimization techniques. Although some academics have concentrated on incorporating single-objective optimization, they frequently encounter issues with reliability and stability when striving to solve problems. In this work, an innovative multi-objective optimization technique that integrates ANFIS with MOPSO_HS is introduced. The model has consistency in problem solving and shows accurate predictions for both odd and even interval input models. In addition, three actual datasets are used to demonstrate the effectiveness of the suggested model's integration. A comparison is made between the suggested integrated model and established algorithms after 20 runs of analysis. The algorithm's accuracy, stability, and dependability in resolving integration problems are demonstrated by the results, which also show how superior it is to alternative approaches.

Anahtar Kelimeler

Metaheuristic Multi-Objective Optimization ANFIS Exchange Rate Neuro Fuzzy RMSE

Kaynakça

  • [1] Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
  • [2] Kar S, Das S, Ghosh PK (2014) Applications of neuro-fuzzy systems: a brief review and future outline. Appl Soft Comput 15:243–259
  • [3] Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353.
  • [4] MOPSO based TCSC–ANFIS–POD technique: Design, simultaneous scheme, power system oscillations suppression AD Falehi - Journal of Intelligent & Fuzzy Systems, 2018.
  • [5] M. Taheri, M.R. Alavi Moghaddam, M. Arami, Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models, Journal of Environmental Management, Volume 128, 2013, Pages 798-806, ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2013.06.029.
  • [6] Karaboga, D., & Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4), 2263-2293.
  • [7] Haznedar, B., & Kalinli, A. (2018). Training ANFIS structure using simulated annealing algorithm for dynamic systems identification. Neurocomputing, 302, 66-74.
  • [8] Marzi, H., Haj Darwish, A., & Helfawi, H. (2017). Training ANFIS using the enhanced Bees Algorithm and least squares estimation. Intelligent Automation & Soft Computing, 23(2), 227-234.
  • [9] Pannu, H. S., Singh, D., & Malhi, A. K. (2019). Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring. Neural computing and applications, 1-11.
  • [10] A Jamali, H Babaei, N Nariman-Zadeh, SH Ashraf Talesh and T Mirzababaie Mostofi (2020). Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications. Vol 234, Issue 3, 2020.368-378 https://doi.org/10.1177/1464420716660332
  • [11] V. Seydi Ghomsheh, M. Aliyari Shoorehdeli, A. Sharifi and M. Teshnehlab, "Multi objective optimization of ANFIS structure," 2007 International Conference on Intelligent and Advanced Systems, Kuala Lumpur, 2007, pp. 249-253, doi: 10.1109/ICIAS.2007.4658384.
  • [12] Carrano, E. G., Takahashi, R. H., Caminhas, W. M., & Neto, O. M. (2008, June). A genetic algorithm for multiobjective training of ANFIS fuzzy networks. In 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence) (pp. 3259-3265). IEEE.
  • [13] Azimi, H., Bonakdari, H., Ebtehaj, I., Talesh, S. H. A., Michelson, D. G., & Jamali, A. (2017). Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets and Systems, 319, 50-69.
  • [14] Qi Liu, Xiaofeng Li, Haitao Liu, Zhaoxia Guo (2020), Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art, Applied Soft Computing,v(93), 1568-4946, 106382
  • [15] Kaisa Miettinen (1999). Nonlinear Multiobjective Optimization. Springer. ISBN 978-0-7923-8278-2. Retrieved 29 May 2012.
  • [16] Jump up to:a b c d e f Ching-Lai Hwang; Abu Syed Md Masud (1979). Multiple objective decision making, methods and applications: a state-of-the-art survey. Springer-Verlag. ISBN 978-0-387-09111-2. Retrieved 29 May 2012.
  • [17] B. Xu, S. Li, A. A. Razzaqi, L. Wang and M. Jiao, "A Novel ANFIS-AQPSO-GA-Based Online Correction Measurement Method for Cooperative Localization," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-17, 2022, Art no. 9504417, doi: 10.1109/TIM.2022.3156997.
  • [18] A. Lefteh, M. Houshmand, M. Khorrampanah and G. F. Smaisim, "Optimization of Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) with Artificial Bee Colony (ABC) Algorithm for Classification of Bone Cancer," 2022 Second International Conference on Distributed Computing and High Performance Computing (DCHPC), 2022, pp. 78-81, doi: 10.1109/DCHPC55044.2022.9731840.
  • [19] N. Srinivas, K. Deb, Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms, Evol Comput, 2 (1994) 221-248.
  • [20] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, Ieee T Evolut Comput, 6 (2002) 182-197.
  • [21] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, in, ETH Zurich, 2001, pp. 21.
  • [22] C.A.C. Coello, G.T. Pulido, M.S. Lechuga, Handling multiple objectives with particle swarm optimization, Ieee T Evolut Comput, 8 (2004) 256-279.
  • [23] R. Hedayatzadeh, B. Hasanizadeh, R. Akbari, K. Ziarati, A multi-objective Artificial Bee Colony for optimizing multi-objective problems, in: 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 2010, pp. V5-277-V275-281.
  • [24] Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert systems with applications, 47, 106-119.
  • [25] Sharma, P., & Sahoo, B. B. (2022). An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine. International Journal of Hydrogen Energy, 47(44), 19298-19318.
  • [26] Nwobi-Okoye, C. C., Ochieze, B. Q., & Okiy, S. (2019). Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite. Journal of Materials Research and Technology, 8(3), 3054-3075.
  • [27] Nguyen, M. D., Nguyen, D. D., Hai, H. N., Sy, A. H., Quang, P. N., Thai, L. N., ... & Pham, B. T. (2023). Estimation of Recompression Coefficient of Soil Using a Hybrid ANFIS-PSO Machine Learning Model. Journal of Engineering Research.
  • [28] Li, J., Yan, G., Abbud, L. H., Alkhalifah, T., Alturise, F., Khadimallah, M. A., & Marzouki, R. (2023). Predicting the shear strength of concrete beam through ANFIS-GA–PSO hybrid modeling. Advances in Engineering Software, 181, 103475.
  • [29] Devaraj, R., Mahalingam, S. K., Esakki, B., Astarita, A., & Mirjalili, S. (2022). A hybrid GA-ANFIS and F-Race tuned harmony search algorithm for Multi-Response optimization of Non-Traditional Machining process. Expert Systems with Applications, 199, 116965.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Aref Yelghi 0000-0003-2380-8718

Erken Görünüm Tarihi 29 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 4 Ocak 2024
Kabul Tarihi 29 Nisan 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 1

Kaynak Göster

IEEE A. Yelghi, “Estimation single output with a hybrid of ANFIS and MOPSO_HS”, SAUCIS, c. 7, sy. 1, ss. 112–126, 2024, doi: 10.35377/saucis...1414742.
 Kapak Resmi İndir

29070  The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License