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Dergiler / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2020 / Cilt: 10 Sayı: 4

Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Mehmet Akpamukçu(İNÖNÜ ÜNİVERSİTESİ)0000-0002-3763-5048
Abdullah Ateş(İNÖNÜ ÜNİVERSİTESİ)0000-0002-4236-6794
Barış Baykant Alagöz(İNÖNÜ ÜNİVERSİTESİ)0000-0001-5238-6433
Sayfa
2451–2460
Yayın Tarihi
2020-12-15
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This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions

📌 3 Maddede Bu Makale

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Amaç & Hipotez:SMDO method is a set and trial based optimization algorithm that is developed for online fine-tuning of controller parameters.
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Temel Bulgular & Veri:However, performance of SMDO has not been evaluated previously for benchmark functions in comparison with other well known heuristic optimization methods.
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Sonuç & Katkı:This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:optimization · SMDO · stochastic

Özet

SMDO method is a set and trial based optimization algorithm that is developed for online fine-tuning of controller parameters. SMDO method is implemented for several controller tuning applications. It can search parameter space with random backward and forward steps of each parameter. This property reduces risk of testing unstable control system configurations in controller design and thus makes the SMDO method more suitable for online parameter tuning of experimental systems. However, performance of SMDO has not been evaluated previously for benchmark functions in comparison with other well known heuristic optimization methods. This study aims to compare performances of Artificial Bee Colony (ABC), Cuckoo Search Optimization (CK), Particle Swarm Optimization (PSO) and Stochastic Multi-parameters Divergence Optimization (SMDO) methods for benchmark functions. Therefore, a benchmark tests program that is a user-friendly MATLAB GUI is introduced for user. This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions

Abstract

SMDO method is a set and trial based optimization algorithm that is developed for online fine-tuning of controller parameters. SMDO method is implemented for several controller tuning applications. It can search parameter space with random backward and forward steps of each parameter. This property reduces risk of testing unstable control system configurations in controller design and thus makes the SMDO method more suitable for online parameter tuning of experimental systems. However, performance of SMDO has not been evaluated previously for benchmark functions in comparison with other well known heuristic optimization methods. This study aims to compare performances of Artificial Bee Colony (ABC), Cuckoo Search Optimization (CK), Particle Swarm Optimization (PSO) and Stochastic Multi-parameters Divergence Optimization (SMDO) methods for benchmark functions. Therefore, a benchmark tests program that is a user-friendly MATLAB GUI is introduced for user. This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions

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Bu Makaleye Atıf Yap

Akpamukçu, M., Ateş, A., Alagöz, B. B. (2020). Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 10 Sayı: 4, 2451–2460. https://doi.org/10.21597/jist.722427

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Kaynakça (24)

  1. Alagoz, B. B., Ates, A., & Yeroglu, C. (2013). Auto-tuning of PID controller according to fractional-order reference model approximation for DC rotor control. Mechatronics, 23(7). https://doi.org/10.1016/j.mechatronics.2013.05.001
  2. Ateş, A. Alagoz, B. B. S. B. C. Y. (2013). Kesir Dereceli PID Kontrolörler İçin Referans Model Tabanlı Optimizasyon Yöntemi. Türkiye Otomatik Kontrol Milli Komitesi 2013 Malatya, 1.
  3. Ates, A., Alagoz, B. B., Chen, Y. Q., Yeroglu, C., & HosseinNia, S. H. (2020). Optimal Fractional Order PID Controller Design for Fractional Order Systems by Stochastic Multi Parameter Divergence Optimization Method with Different Random Distribution Functions. 9–14. https://doi.org/10.1109/iccma46720.2019.8988599
  4. Ates, A., Alagoz, B. B., & Yeroglu, C. (2017). Master–slave stochastic optimization for model-free controller tuning. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 41(2). https://doi.org/10.1007/s40998-017-0029-1
  5. Ateş, A., & Yeroglu, C. (n.d.). SMDO Algoritması ile İki Serbestlik Dereceli FOPID Kontrol Çevrimi Tasarımı Two Degrees of Freedom FOPID Control Loop Design via SMDO Algorithm. 6–11.
  6. Ates, Abdullah, AlagOz, B. B., Tepljakov, A., Petlenkov, E., Yeroglu, C., Kuznetsov, A., & Sobolev, I. (2019). Fractional Order Model Identification of Receptor-Ligand Complexes Formation by Equivalent Electrical Circuit Modeling. 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019. https://doi.org/10.1109/IDAP.2019.8875913
  7. Ates, Abdullah, & YEROĞLU, C. (2016). Online Tuning of Two Degrees of Freedom Fractional Order Control Loops. Balkan Journal of Electrical and Computer Engineering, 4(1), 5–11. https://doi.org/10.17694/bajece.52491
  8. Ates, Abdullah, Yeroglu, C., Alagoz, B. B., & Senol, B. (2014). Tuning of fractional order PID with master-slave stochastic multi-parameter divergence optimization method. 2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014. https://doi.org/10.1109/ICFDA.2014.6967388
  9. Biswas, A., Mishra, K. K., Tiwari, S., & Misra, A. K. (2013). Physics-Inspired Optimization Algorithms: A Survey. Journal of Optimization. https://doi.org/10.1155/2013/438152
  10. Chopard, B., & Tomassini, M. (2018). Particle swarm optimization. In Natural Computing Series. https://doi.org/10.1007/978-3-319-93073-2_6
  11. Civicioglu, P., & Besdok, E. (2013). A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review. https://doi.org/10.1007/s10462-011-9276-0
  12. Emmerich, M. T. M., & Deutz, A. H. (2018). A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural Computing. https://doi.org/10.1007/s11047-018-9685-y
  13. Frédéric Bonnans, J., Charles Gilbert, J., Lemaréchal, C., & Sagastizábal, C. A. (2006). Numerical optimization: Theoretical and practical aspects. In Numerical Optimization: Theoretical and Practical Aspects. https://doi.org/10.1007/978-3-540-35447-5
  14. Giagkiozis, I., & Fleming, P. J. (2015). Methods for multi-objective optimization: An analysis. Information Sciences. https://doi.org/10.1016/j.ins.2014.08.071
  15. Kakandikar, G. M., Nandedkar, V. M., Kakandikar, G. M., & Nandedkar, V. M. (2018). Engineering Optimization. In Sheet Metal Forming Optimization. https://doi.org/10.4324/9781315156101-4
  16. Kuhn, H. W., & Tucker, A. W. (2014). Nonlinear programming. In Traces and Emergence of Nonlinear Programming. https://doi.org/10.1007/978-3-0348-0439-4_11
  17. 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. https://doi.org/10.1016/j.advengsoft.2017.07.002
  18. Nocedal, J., & Wright, S. (2006). Numerical optimization, series in operations research and financial engineering. In Springer.
  19. Rao, S. S. (2009). Engineering Optimization: Theory and Practice: Fourth Edition. In Engineering Optimization: Theory and Practice: Fourth Edition. https://doi.org/10.1002/9780470549124
  20. Unconstained. (n.d.). Retrieved April 16, 2020, from http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO_files/Page364.htm
  21. Vajda, S., & Dantzig, G. B. (1965). Linear Programming and Extensions. The Mathematical Gazette. https://doi.org/10.2307/3612922
  22. Yang, X. S. (2014). Nature-Inspired Optimization Algorithms. In Nature-Inspired Optimization Algorithms. https://doi.org/10.1016/C2013-0-01368-0
  23. Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations (Swansea, Wales). https://doi.org/10.1108/02644401211235834
  24. Yeroǧlu, C., & Ateş, A. (2014). A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers. Journal of the Franklin Institute, 351(5). https://doi.org/10.1016/j.jfranklin.2013.12.006.