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Gradyan Tabanlı Optimize Edici Algoritmasının Parametre Ayarlaması

Year 2021, Issue: 28, 727 - 742, 30.11.2021
https://doi.org/10.31590/ejosat.1010813

Abstract

Bu çalışmada, popülasyon tabanlı yeni bir meta-sezgisel optimizasyon algoritması olan gradyan tabanlı optimize edici (GBO) algoritmasının olasılık parametresi ayarı yapılmıştır. Gradyan tabanlı Newton yönteminden ilham alan GBO, gradyan arama kuralı (GAK) ve yerel kaçış operatörü (YKO) olmak üzere iki ana operatör kullanır. Gradyan arama kuralında, uygulanabilir alanda daha iyi arama yapmak ve daha iyi konumlar elde etmek için vektörlerin hareketi kontrol edilir. Arama eğilimini arttırmak ve GBO'nun yakınsamasını hızlandırmak amacıyla, GAK, gardyan tabanlı (GT) yöntemi konseptine dayalı olarak önerilmiştir. GAK, arama uzayında daha iyi konumlar elde etmek için keşif eğilimini geliştirmek ve yakınsama oranını hızlandırmak için gradyan tabanlı yöntemi kullanır. YKO operatörü, çözümün konumunu önemli ölçüde değiştirebilmektedir. YKO operatöründe rastgele değerlerle kıyaslanmak üzere olasılık parametresi (pr∈ (0,1)) kullanılmaktadır. Olasılık parametresi GBO’nun çalışma performansına etkisini anlayabilmek için on iki adet tek modlu ve on iki adet çok modlu test fonksiyonları kullanılmıştır. Deneysel çalışmalarda olasılık parametre değerleri sırasıyla 0,1, 0,3, 0,5, 0,7 ve 0,9 olarak alınmıştır. GBO algoritmasında olasılık parametresinin önemli bir faktör olduğu ve GBO’nun çalışma performansını önemli ölçüde etkilediği belirlenmiştir. Ek olarak olasılık parametresinin 0,9 değerine yaklaştığında GBO’nun tek modlu ve çok modlu test fonksiyon sonuçlarında daha iyi değerler hesapladığı grafik ve tablolarla gösterilmiştir.

References

  • Aala Kalananda, V. K. R., & Komanapalli, V. L. N. (2021). A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems. Applied Soft Computing, 99, 106903. doi:https://doi.org/10.1016/j.asoc.2020.106903
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  • Beşkirli, A., & Dağ, İ. (2020). A new binary variant with transfer functions of Harris Hawks Optimization for binary wind turbine micrositing. Energy Reports, 6, 668-673. doi:https://doi.org/10.1016/j.egyr.2020.11.154
  • Beşkirli, A., Özdemir, D., & Temurtaş, H. (2020). A comparison of modified tree–seed algorithm for high-dimensional numerical functions. Neural Computing and Applications, 32(11), 6877-6911. doi:10.1007/s00521-019-04155-3
  • Beşkirli, A., Temurtaş, H., & Özdemir, D. (2020). Determination with Linear Form of Turkey's Energy Demand Forecasting by the Tree Seed Algorithm and the Modified Tree Seed Algorithm. Advances in Electrical and Computer Engineering, 20(2), 27-34. doi:10.4316/AECE.2020.02004
  • Dhiman, G., & Kaur, A. (2019). STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148-174. doi:https://doi.org/10.1016/j.engappai.2019.03.021
  • Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731. doi:https://doi.org/10.1016/j.engappai.2020.103731
  • Huerta, I. I., Neira, D. A., Ortega, D. A., Varas, V., Godoy, J., & Asín-Achá, R. (2022). Improving the state-of-the-art in the Traveling Salesman Problem: An Anytime Automatic Algorithm Selection. Expert Systems with Applications, 187, 115948. doi:https://doi.org/10.1016/j.eswa.2021.115948
  • Kıran, M. S., & Fındık, O. (2015). A directed artificial bee colony algorithm. Applied Soft Computing, 26, 454-462. doi:https://doi.org/10.1016/j.asoc.2014.10.020
  • Kutlu Onay, F., & Aydemı̇r, S. B. (2022). Chaotic hunger games search optimization algorithm for global optimization and engineering problems. Mathematics and Computers in Simulation, 192, 514-536. doi:https://doi.org/10.1016/j.matcom.2021.09.014
  • Makhloufi, S. (2015). Comparative study between classical methods and genetic algorithms for sizing remote PV systems. International Journal of Energy and Environmental Engineering, 6(3), 221-231. doi:10.1007/s40095-015-0170-4
  • Salgotra, R., Singh, U., Singh, G., Mittal, N., & Gandomi, A. H. (2021). A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 383, 113916. doi:https://doi.org/10.1016/j.cma.2021.113916
  • Shabani, A., Asgarian, B., Salido, M., & Asil Gharebaghi, S. (2020). Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications, 161, 113698. doi:https://doi.org/10.1016/j.eswa.2020.113698
  • Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2020). Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103330. doi:https://doi.org/10.1016/j.engappai.2019.103330
  • Umam, M. S., Mustafid, M., & Suryono, S. (2021). A hybrid genetic algorithm and tabu search for minimizing makespan in flow shop scheduling problem. Journal of King Saud University - Computer and Information Sciences. doi:https://doi.org/10.1016/j.jksuci.2021.08.025

Parameter Adjustment of Gradient Based Optimizer Algorithm

Year 2021, Issue: 28, 727 - 742, 30.11.2021
https://doi.org/10.31590/ejosat.1010813

Abstract

In this study, the probability parameter of the gradient-based optimizer (GBO) algorithm, which is a new population-based meta-heuristic optimization algorithm, is adjusted. Inspired by the gradient-based Newtonian method, GBO uses two main operators, the gradient search rule (GSR) and the local escape operator (LEO). In the gradient search rule, the movement of vectors is controlled in order to better search and obtain better positions in the applicable area. In order to increase search propensity and accelerate the convergence of GBO, GSR is proposed based on the concept of the guard-based (GT) method. GSR uses the gradient-based method to improve the exploration propensity and speed up the convergence rate to get better positions in the search space. The LEO operator can significantly change the position of the solution. In the LEO operator, the probability parameter (pr∈ (0,1)) is used to compare with random values. In order to understand the effect of probability parameter GBO on operating performance, twelve single-mode and twelve multi-mode benchmark functions are used. In experimental studies, probability parameter values were taken as 0.1, 0.3, 0.5, 0.7 and 0.9, respectively. It has been determined that the probability parameter is an important factor in the GBO algorithm and it significantly affects the working performance of the GBO. In addition, graphs and tables show that GBO calculates better values in unimodal and multimodal benchmark function results when the probability parameter approaches 0.9.

References

  • Aala Kalananda, V. K. R., & Komanapalli, V. L. N. (2021). A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems. Applied Soft Computing, 99, 106903. doi:https://doi.org/10.1016/j.asoc.2020.106903
  • Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159. doi:https://doi.org/10.1016/j.ins.2020.06.037
  • Akay, B., & Karaboga, D. (2012). A modified Artificial Bee Colony algorithm for real-parameter optimization. Information Sciences, 192, 120-142. doi:https://doi.org/10.1016/j.ins.2010.07.015
  • Alavidoost, M. H., Zarandi, M. H. F., Tarimoradi, M., & Nemati, Y. (2017). Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times. Journal of Intelligent Manufacturing, 28(2), 313-336. doi:10.1007/s10845-014-0978-4
  • Beskirli, A., Beskirli, M., Hakli, H., & Uguz, H. (2018). Comparing energy demand estimation using artificial algae algorithm: The case of Turkey. Journal of Clean Energy Technologies, 6(4), 349-352. doi:10.18178/jocet.2018.6.4.487
  • Beşkirli, A., & Dağ, İ. (2020). A new binary variant with transfer functions of Harris Hawks Optimization for binary wind turbine micrositing. Energy Reports, 6, 668-673. doi:https://doi.org/10.1016/j.egyr.2020.11.154
  • Beşkirli, A., Özdemir, D., & Temurtaş, H. (2020). A comparison of modified tree–seed algorithm for high-dimensional numerical functions. Neural Computing and Applications, 32(11), 6877-6911. doi:10.1007/s00521-019-04155-3
  • Beşkirli, A., Temurtaş, H., & Özdemir, D. (2020). Determination with Linear Form of Turkey's Energy Demand Forecasting by the Tree Seed Algorithm and the Modified Tree Seed Algorithm. Advances in Electrical and Computer Engineering, 20(2), 27-34. doi:10.4316/AECE.2020.02004
  • Dhiman, G., & Kaur, A. (2019). STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148-174. doi:https://doi.org/10.1016/j.engappai.2019.03.021
  • Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731. doi:https://doi.org/10.1016/j.engappai.2020.103731
  • Huerta, I. I., Neira, D. A., Ortega, D. A., Varas, V., Godoy, J., & Asín-Achá, R. (2022). Improving the state-of-the-art in the Traveling Salesman Problem: An Anytime Automatic Algorithm Selection. Expert Systems with Applications, 187, 115948. doi:https://doi.org/10.1016/j.eswa.2021.115948
  • Kıran, M. S., & Fındık, O. (2015). A directed artificial bee colony algorithm. Applied Soft Computing, 26, 454-462. doi:https://doi.org/10.1016/j.asoc.2014.10.020
  • Kutlu Onay, F., & Aydemı̇r, S. B. (2022). Chaotic hunger games search optimization algorithm for global optimization and engineering problems. Mathematics and Computers in Simulation, 192, 514-536. doi:https://doi.org/10.1016/j.matcom.2021.09.014
  • Makhloufi, S. (2015). Comparative study between classical methods and genetic algorithms for sizing remote PV systems. International Journal of Energy and Environmental Engineering, 6(3), 221-231. doi:10.1007/s40095-015-0170-4
  • Salgotra, R., Singh, U., Singh, G., Mittal, N., & Gandomi, A. H. (2021). A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 383, 113916. doi:https://doi.org/10.1016/j.cma.2021.113916
  • Shabani, A., Asgarian, B., Salido, M., & Asil Gharebaghi, S. (2020). Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications, 161, 113698. doi:https://doi.org/10.1016/j.eswa.2020.113698
  • Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2020). Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103330. doi:https://doi.org/10.1016/j.engappai.2019.103330
  • Umam, M. S., Mustafid, M., & Suryono, S. (2021). A hybrid genetic algorithm and tabu search for minimizing makespan in flow shop scheduling problem. Journal of King Saud University - Computer and Information Sciences. doi:https://doi.org/10.1016/j.jksuci.2021.08.025
There are 18 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mehmet Beşkirli 0000-0002-4842-3817

Mehmet Fatih Tefek 0000-0003-3390-4201

Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 28

Cite

APA Beşkirli, M., & Tefek, M. F. (2021). Gradyan Tabanlı Optimize Edici Algoritmasının Parametre Ayarlaması. Avrupa Bilim Ve Teknoloji Dergisi(28), 727-742. https://doi.org/10.31590/ejosat.1010813