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Performance Enhancement of Distribution Function Based Monarch Butterfly Optimization Algorithm with Optimization to Optimization Approach

Yıl 2021, Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special, 100 - 108, 20.10.2021
https://doi.org/10.53070/bbd.990245

Öz

In this study, the parameters of the distribution functions were adjusted with the optimization to optimization approach to improve the performance of the distribution function-based monarch butterfly optimization algorithm (MBO). For this, the random number generation processes, which greatly affect the flow of stochastic algorithms, were examined and the effect of distribution functions on these processes was determined. Then, the importance of parameter selection in the operation of distribution functions has been determined. It has been seen that the distribution function will be more effective with appropriate parameter selections. At this point, the distribution functions that can be used in the random number generation in the main target algorithm were tried to be determined with appropriate parameters with an upper auxiliary optimization algorithm. In conclusion; With the approach of optimization to optimization, the performance of the target algorithm has been tried to be increased and concrete results are presented in comparison with the tests made on the most used benchmark functions in the literature.

Kaynakça

  • Akdag, O., Ates, A., & Yeroglu, C. (2020). Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem. Neural Computing and Applications, 5. https://doi.org/10.1007/s00521-020-05073-5
  • Ates, A. (2021). Enhanced equilibrium optimization method with fractional order chaotic and application engineering. Neural Computing and Applications, 1–28. https://doi.org/10.1007/s00521-021-05756-7
  • Ates, A., & Akpamukcu, M. (2021). Optimization to optimization (OtoO): optimize monarchy butterfly method with stochastics multi-parameter divergence method for benchmark functions and load frequency control. Engineering with Computers. https://doi.org/10.1007/s00366-021-01364-0
  • Ates, A., Alagoz, B. B., Kavuran, G., & Yeroglu, C. (2017). Implementation of fractional order filters discretized by modified Fractional Order Darwinian Particle Swarm Optimization. Measurement: Journal of the International Measurement Confederation. https://doi.org/10.1016/j.measurement.2017.05.017
  • Ateş, A., & Yeroglu, C. (2016). Optimal fractional order PID design via Tabu Search based algorithm. ISA Transactions, 60. https://doi.org/10.1016/j.isatra.2015.11.015
  • Ateş, Abdullah, & Yeroğlu, C. (2018). Modified Artificial Physics Optimization for Multi-parameter Functions. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 42(4), 465–478. https://doi.org/10.1007/s40998-018-0082-4
  • Elbes, M., Alzubi, S., Kanan, T., Al-Fuqaha, A., & Hawashin, B. (2019). A survey on particle swarm optimization with emphasis on engineering and network applications. In Evolutionary Intelligence (Vol. 12, Issue 2, pp. 113–129). Springer Verlag. https://doi.org/10.1007/s12065-019-00210-z
  • Gaidhane, P. J., & Nigam, M. J. (2018). A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems. Journal of Computational Science. https://doi.org/10.1016/j.jocs.2018.06.008
  • Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing. https://doi.org/10.1287/ijoc.1.3.190
  • Glover, F. (1990). Tabu Search—Part II. ORSA Journal on Computing. https://doi.org/10.1287/ijoc.2.1.4
  • Guha, D., Roy, P. K., & Banerjee, S. (2020). Grasshopper optimization algorithm scaled fractional order PI-D controller applied to reduced order model of load frequency control system. International Journal of Modelling and Simulation. https://doi.org/10.1080/02286203.2019.1596727
  • Khadanga, R. K., Kumar, A., & Panda, S. (2020). A novel modified whale optimization algorithm for load frequency controller design of a two-area power system composing of PV grid and thermal generator. Neural Computing and Applications. https://doi.org/10.1007/s00521-019-04321-7
  • Liang, X., Kou, D., & Wen, L. (2020). An Improved Chicken Swarm Optimization Algorithm and its Application in Robot Path Planning. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2974498
  • Probability Density Function. (2020). Retrieved May 9, 2020, from https://www.mathworks.com/help/stats/prob.normaldistribution.pdf.html
  • van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated Annealing: Theory and Applications. In Simulated Annealing: Theory and Applications. https://doi.org/10.1007/978-94-015-7744-1
  • Wang, G. G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/s00521-015-1923-y
  • 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), 2411–2429. https://doi.org/10.1016/j.jfranklin.2013.12.006

Optimizasyonun Optimizasyonu Yaklaşımıyla Dağılım Fonksiyonu Tabanlı Kral Kelebeği Optimizasyon Algoritmasının Performansının Artırılması

Yıl 2021, Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special, 100 - 108, 20.10.2021
https://doi.org/10.53070/bbd.990245

Öz

Bu çalışmada, dağılım fonksiyonu tabanlı kral kelebek optimizasyon algoritmasının (KKO) performansını iyileştirmek için optimizasyonun optimizasyonu yaklaşımıyla dağılım fonksiyonlarının parametreleri ayarlanmıştır. Bunun için stokastik algoritmaların akışını büyük ölçüde etkileyen rastgele sayı üretme süreçleri incelenmiş ve dağılım fonksiyonlarının bu süreçlere etkisi belirlenmiştir. Daha sonra dağılım fonksiyonlarının işleyişinde parametre seçiminin önemi belirlenmiştir. Uygun parametre seçimleri ile dağılım fonksiyonunun daha etkin olacağı görülmüştür. Bu noktada ana hedef algoritmada rastgele sayı üretiminde kullanılabilecek uygun parametreli dağılım fonksiyonları, bir üst yardımcı optimizasyon algoritması ile belirlenmeye çalışılmıştır. Sonuç olarak; optimizasyonun optimizasyonu yaklaşımı ile hedef algoritmanın performansı artırılmaya çalışılmış ve literatürde en çok kullanılan benchmark fonksiyonları üzerinde yapılan testler ile karşılaştırmalı olarak somut sonuçlar sunulmuştur.

Kaynakça

  • Akdag, O., Ates, A., & Yeroglu, C. (2020). Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem. Neural Computing and Applications, 5. https://doi.org/10.1007/s00521-020-05073-5
  • Ates, A. (2021). Enhanced equilibrium optimization method with fractional order chaotic and application engineering. Neural Computing and Applications, 1–28. https://doi.org/10.1007/s00521-021-05756-7
  • Ates, A., & Akpamukcu, M. (2021). Optimization to optimization (OtoO): optimize monarchy butterfly method with stochastics multi-parameter divergence method for benchmark functions and load frequency control. Engineering with Computers. https://doi.org/10.1007/s00366-021-01364-0
  • Ates, A., Alagoz, B. B., Kavuran, G., & Yeroglu, C. (2017). Implementation of fractional order filters discretized by modified Fractional Order Darwinian Particle Swarm Optimization. Measurement: Journal of the International Measurement Confederation. https://doi.org/10.1016/j.measurement.2017.05.017
  • Ateş, A., & Yeroglu, C. (2016). Optimal fractional order PID design via Tabu Search based algorithm. ISA Transactions, 60. https://doi.org/10.1016/j.isatra.2015.11.015
  • Ateş, Abdullah, & Yeroğlu, C. (2018). Modified Artificial Physics Optimization for Multi-parameter Functions. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 42(4), 465–478. https://doi.org/10.1007/s40998-018-0082-4
  • Elbes, M., Alzubi, S., Kanan, T., Al-Fuqaha, A., & Hawashin, B. (2019). A survey on particle swarm optimization with emphasis on engineering and network applications. In Evolutionary Intelligence (Vol. 12, Issue 2, pp. 113–129). Springer Verlag. https://doi.org/10.1007/s12065-019-00210-z
  • Gaidhane, P. J., & Nigam, M. J. (2018). A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems. Journal of Computational Science. https://doi.org/10.1016/j.jocs.2018.06.008
  • Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing. https://doi.org/10.1287/ijoc.1.3.190
  • Glover, F. (1990). Tabu Search—Part II. ORSA Journal on Computing. https://doi.org/10.1287/ijoc.2.1.4
  • Guha, D., Roy, P. K., & Banerjee, S. (2020). Grasshopper optimization algorithm scaled fractional order PI-D controller applied to reduced order model of load frequency control system. International Journal of Modelling and Simulation. https://doi.org/10.1080/02286203.2019.1596727
  • Khadanga, R. K., Kumar, A., & Panda, S. (2020). A novel modified whale optimization algorithm for load frequency controller design of a two-area power system composing of PV grid and thermal generator. Neural Computing and Applications. https://doi.org/10.1007/s00521-019-04321-7
  • Liang, X., Kou, D., & Wen, L. (2020). An Improved Chicken Swarm Optimization Algorithm and its Application in Robot Path Planning. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2974498
  • Probability Density Function. (2020). Retrieved May 9, 2020, from https://www.mathworks.com/help/stats/prob.normaldistribution.pdf.html
  • van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated Annealing: Theory and Applications. In Simulated Annealing: Theory and Applications. https://doi.org/10.1007/978-94-015-7744-1
  • Wang, G. G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/s00521-015-1923-y
  • 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), 2411–2429. https://doi.org/10.1016/j.jfranklin.2013.12.006
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yazılım Testi, Doğrulama ve Validasyon
Bölüm PAPERS
Yazarlar

Mehmet Akpamukçu 0000-0002-3763-5048

Abdullah Ateş 0000-0002-4236-6794

Yayımlanma Tarihi 20 Ekim 2021
Gönderilme Tarihi 3 Eylül 2021
Kabul Tarihi 16 Eylül 2021
Yayımlandığı Sayı Yıl 2021 Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special

Kaynak Göster

APA Akpamukçu, M., & Ateş, A. (2021). Optimizasyonun Optimizasyonu Yaklaşımıyla Dağılım Fonksiyonu Tabanlı Kral Kelebeği Optimizasyon Algoritmasının Performansının Artırılması. Computer Science, IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium(Special), 100-108. https://doi.org/10.53070/bbd.990245

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