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Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Yıl 2020, Cilt: 10 Sayı: 4, 2451 - 2460, 15.12.2020

Mehmet Akpamukçu , Abdullah Ateş , Barış Baykant Alagöz

https://doi.org/10.21597/jist.722427

Öz

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

Anahtar Kelimeler

SMDO optimization stochastic benchmark functions

Kaynakça

  • 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
  • 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.
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Chopard, B., & Tomassini, M. (2018). Particle swarm optimization. In Natural Computing Series. https://doi.org/10.1007/978-3-319-93073-2_6
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Nocedal, J., & Wright, S. (2006). Numerical optimization, series in operations research and financial engineering. In Springer.
  • 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
  • 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
  • Vajda, S., & Dantzig, G. B. (1965). Linear Programming and Extensions. The Mathematical Gazette. https://doi.org/10.2307/3612922
  • Yang, X. S. (2014). Nature-Inspired Optimization Algorithms. In Nature-Inspired Optimization Algorithms. https://doi.org/10.1016/C2013-0-01368-0
  • 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
  • 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.

Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Yıl 2020, Cilt: 10 Sayı: 4, 2451 - 2460, 15.12.2020

Mehmet Akpamukçu , Abdullah Ateş , Barış Baykant Alagöz

https://doi.org/10.21597/jist.722427

Öz

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

Anahtar Kelimeler

optimization SMDO stochastic benchmark functions

Kaynakça

  • 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
  • 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.
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Chopard, B., & Tomassini, M. (2018). Particle swarm optimization. In Natural Computing Series. https://doi.org/10.1007/978-3-319-93073-2_6
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Nocedal, J., & Wright, S. (2006). Numerical optimization, series in operations research and financial engineering. In Springer.
  • 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
  • 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
  • Vajda, S., & Dantzig, G. B. (1965). Linear Programming and Extensions. The Mathematical Gazette. https://doi.org/10.2307/3612922
  • Yang, X. S. (2014). Nature-Inspired Optimization Algorithms. In Nature-Inspired Optimization Algorithms. https://doi.org/10.1016/C2013-0-01368-0
  • 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
  • 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.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Elektrik Elektronik Mühendisliği / Electrical Electronic Engineering
Yazarlar

Mehmet Akpamukçu 0000-0002-3763-5048

Abdullah Ateş 0000-0002-4236-6794

Barış Baykant Alagöz 0000-0001-5238-6433

Yayımlanma Tarihi 15 Aralık 2020
Gönderilme Tarihi 17 Nisan 2020
Kabul Tarihi 25 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 10 Sayı: 4

Kaynak Göster

APA Akpamukçu, M., Ateş, A., & Alagöz, B. B. (2020). Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox. Journal of the Institute of Science and Technology, 10(4), 2451-2460. https://doi.org/10.21597/jist.722427
AMA Akpamukçu M, Ateş A, Alagöz BB. Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox. Iğdır Üniv. Fen Bil Enst. Der. Aralık 2020;10(4):2451-2460. doi:10.21597/jist.722427
Chicago Akpamukçu, Mehmet, Abdullah Ateş, ve Barış Baykant Alagöz. “Performance Analysis of SMDO Method With Benchmark Functions With Matlab Toolbox”. Journal of the Institute of Science and Technology 10, sy. 4 (Aralık 2020): 2451-60. https://doi.org/10.21597/jist.722427.
EndNote Akpamukçu M, Ateş A, Alagöz BB (01 Aralık 2020) Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox. Journal of the Institute of Science and Technology 10 4 2451–2460.
IEEE M. Akpamukçu, A. Ateş, ve B. B. Alagöz, “Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox”, Iğdır Üniv. Fen Bil Enst. Der., c. 10, sy. 4, ss. 2451–2460, 2020, doi: 10.21597/jist.722427.
ISNAD Akpamukçu, Mehmet vd. “Performance Analysis of SMDO Method With Benchmark Functions With Matlab Toolbox”. Journal of the Institute of Science and Technology 10/4 (Aralık 2020), 2451-2460. https://doi.org/10.21597/jist.722427.
JAMA Akpamukçu M, Ateş A, Alagöz BB. Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox. Iğdır Üniv. Fen Bil Enst. Der. 2020;10:2451–2460.
MLA Akpamukçu, Mehmet vd. “Performance Analysis of SMDO Method With Benchmark Functions With Matlab Toolbox”. Journal of the Institute of Science and Technology, c. 10, sy. 4, 2020, ss. 2451-60, doi:10.21597/jist.722427.
Vancouver Akpamukçu M, Ateş A, Alagöz BB. Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox. Iğdır Üniv. Fen Bil Enst. Der. 2020;10(4):2451-60.