Araştırma Makalesi
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Yıl 2022, Cilt: 34 Sayı: 1, 85 - 104, 20.03.2022

Salih Berkan Aydemir

https://doi.org/10.35234/fumbd.969335
Cited By: 3

Öz

Kaynakça

  • [1] Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PloS one, 10(5), e0122827.
  • [2] Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. [3] Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 107250.
  • [4] Altay, E. V., & Alatas, B. (2020). Bird swarm algorithms with chaotic mapping. Artificial Intelligence Review, 53(2), 1373-1414.
  • [5] Anthony, M., & Bartlett, P. L. (2009). Neural network learning: Theoretical foundations: cambridge university press.
  • [6] Booker, L. B., Goldberg, D. E., & Holland, J. H. (1989). Classifier systems and genetic algorithms. Artificial intelligence, 40(1-3), 235-282.
  • [7] Carnie, S. K. (1954). Food habits of nesting golden eagles in the coast ranges of California. The Condor, 56(1), 3-12.
  • [8] Črepinšek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 45(3), 1-33.
  • [9] Dekker, D. (1985). HUNTING BEHAVIOR OF GOLDEN EAGLES, AQUILA-CHRYSAETOS, MIGRATING IN SOUTHWESTERN ALBERTA (Vol. 99, pp. 383-385): OTTAWA FIELD-NATURALISTS CLUB PO BOX 35069, WESTGATE PO, OTTAWA ON K1Z 1A2 ….
  • [10] Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
  • [11] dos Santos Coelho, L., & Mariani, V. C. (2008). Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Systems with Applications, 34(3), 1905-1913.
  • [12] Dunia, S., & Ramzy, S. (2018). Chaotic Sine-Cosine Algorithms. International Journal of Soft Computing, 13(3), 108-122.
  • [13] Eubank, S., & Farmer, D. (1990). An introduction to chaos and randomness 1989 lectures in complex systems. Proceedings: Lectures, Volume 2.
  • [14] García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15(6), 617-644.
  • [15] Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation, 11(1), 1-18.
  • [16] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
  • [17] Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
  • [18] Hussien, A. G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., & Chen, H. (2020). Crow search algorithm: theory, recent advances, and applications. IEEE Access, 8, 173548-173565.
  • [19] Jang, J.-S., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378-406.
  • [20] Jenkinson, O. (2019). Ergodic optimization in dynamical systems. Ergodic Theory and Dynamical Systems, 39(10), 2593-2618.
  • [21] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
  • [22] Kaur, G., & Arora, S. (2018). Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275-284.
  • [23] Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3), 267-289.
  • [24] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-international conference on neural networks.
  • [25] Kiefer, J. (1953). Sequential minimax search for a maximum. Proceedings of the American mathematical society, 4(3), 502-506.
  • [26] Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5(4), 458-472.
  • [27] Koupaei, J. A., Hosseini, S. M. M., & Ghaini, F. M. (2016). A new optimization algorithm based on chaotic maps and golden section search method. Engineering Applications of Artificial Intelligence, 50, 201-214.
  • [28] Koza, J. R., & Rice, J. P. (1992). Automatic programming of robots using genetic programming. Paper presented at the AAAI.
  • [29] Meinertzhagen, R. (1940). How do larger raptorial birds hunt their prey. Ibis, 4, 530-535.
  • [30] Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.
  • [31] Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133.
  • [32] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
  • [33] Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
  • [34] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • [35] Molga, M., & Smutnicki, C. (2016). Test functions for optimization needs, 2005. URL http://www. zsd. ict. pwr. wroc. pl/files/docs/functions. pdf.
  • [36] Ollagnier, J. M. (2007). Ergodic theory and statistical mechanics (Vol. 1115): Springer.
  • [37] Osman, I. H., & Kelly, J. P. (1997). Meta-heuristics theory and applications. Journal of the Operational Research Society, 48(6), 657-657.
  • [38] Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
  • [39] Steenhof, K., Kochert, M. N., & Mcdonald, T. L. (1997). Interactive effects of prey and weather on golden eagle reproduction. Journal of Animal Ecology, 350-362.
  • [40] Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
  • [41] Watson, J. (2010). The golden eagle: Bloomsbury Publishing.
  • [42] Wilcoxon, F. (1992). Individual comparisons by ranking methods Breakthroughs in statistics (pp. 196-202): Springer.
  • [43] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary computation, 1(1), 67-82.
  • [44] Yang, X.-S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.

Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu

Yıl 2022, Cilt: 34 Sayı: 1, 85 - 104, 20.03.2022

Salih Berkan Aydemir

https://doi.org/10.35234/fumbd.969335
Cited By: 3

Öz

Bu çalışmada kartal (Aquila) optimizasyon algoritmasındaki rastgele değişkenler, Gauss kaotik haritası ile değiştirilmektedir. Kaotik haritaların tekrar edilememezlik özelliği ile küresel optimum noktaya yakınsama durumu incelenmektedir. Gauss kaotik haritası, çözüm uzayının farklı noktalarını ele alıp, algoritmanın yerel optimum noktada takılmasını önleyebilmektedir. Önerilen kaotik kartal optimizasyonu 13 kıyaslamalı test fonksiyonu üzerinde test edilmiştir. 13 test fonksiyonu içerisinde, 12 test fonksiyonunda yeni Gauss tabanlı kaotik kartal optimizasyonunun klasik kartal optimizasyonuna göre daha iyi yakınsama gösterdiği görülmüştür. Ek olarak önerilen kaotik tabanlı kartal optimizasyonu ile üç test fonksiyonunda, küresel optimum noktaya yakınsamaktadır. Önerilen algoritma ve klasik algoritmanın yakınsama eğrileri, grafikler halinde özetlenmiştir.

Anahtar Kelimeler

Meta-heuristic algorithms Benchmark functions Optimization Chaotic maps Chaos theory

Kaynakça

  • [1] Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PloS one, 10(5), e0122827.
  • [2] Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. [3] Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 107250.
  • [4] Altay, E. V., & Alatas, B. (2020). Bird swarm algorithms with chaotic mapping. Artificial Intelligence Review, 53(2), 1373-1414.
  • [5] Anthony, M., & Bartlett, P. L. (2009). Neural network learning: Theoretical foundations: cambridge university press.
  • [6] Booker, L. B., Goldberg, D. E., & Holland, J. H. (1989). Classifier systems and genetic algorithms. Artificial intelligence, 40(1-3), 235-282.
  • [7] Carnie, S. K. (1954). Food habits of nesting golden eagles in the coast ranges of California. The Condor, 56(1), 3-12.
  • [8] Črepinšek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 45(3), 1-33.
  • [9] Dekker, D. (1985). HUNTING BEHAVIOR OF GOLDEN EAGLES, AQUILA-CHRYSAETOS, MIGRATING IN SOUTHWESTERN ALBERTA (Vol. 99, pp. 383-385): OTTAWA FIELD-NATURALISTS CLUB PO BOX 35069, WESTGATE PO, OTTAWA ON K1Z 1A2 ….
  • [10] Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
  • [11] dos Santos Coelho, L., & Mariani, V. C. (2008). Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Systems with Applications, 34(3), 1905-1913.
  • [12] Dunia, S., & Ramzy, S. (2018). Chaotic Sine-Cosine Algorithms. International Journal of Soft Computing, 13(3), 108-122.
  • [13] Eubank, S., & Farmer, D. (1990). An introduction to chaos and randomness 1989 lectures in complex systems. Proceedings: Lectures, Volume 2.
  • [14] García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15(6), 617-644.
  • [15] Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation, 11(1), 1-18.
  • [16] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
  • [17] Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
  • [18] Hussien, A. G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., & Chen, H. (2020). Crow search algorithm: theory, recent advances, and applications. IEEE Access, 8, 173548-173565.
  • [19] Jang, J.-S., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378-406.
  • [20] Jenkinson, O. (2019). Ergodic optimization in dynamical systems. Ergodic Theory and Dynamical Systems, 39(10), 2593-2618.
  • [21] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
  • [22] Kaur, G., & Arora, S. (2018). Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275-284.
  • [23] Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3), 267-289.
  • [24] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-international conference on neural networks.
  • [25] Kiefer, J. (1953). Sequential minimax search for a maximum. Proceedings of the American mathematical society, 4(3), 502-506.
  • [26] Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5(4), 458-472.
  • [27] Koupaei, J. A., Hosseini, S. M. M., & Ghaini, F. M. (2016). A new optimization algorithm based on chaotic maps and golden section search method. Engineering Applications of Artificial Intelligence, 50, 201-214.
  • [28] Koza, J. R., & Rice, J. P. (1992). Automatic programming of robots using genetic programming. Paper presented at the AAAI.
  • [29] Meinertzhagen, R. (1940). How do larger raptorial birds hunt their prey. Ibis, 4, 530-535.
  • [30] Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.
  • [31] Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133.
  • [32] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
  • [33] Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
  • [34] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • [35] Molga, M., & Smutnicki, C. (2016). Test functions for optimization needs, 2005. URL http://www. zsd. ict. pwr. wroc. pl/files/docs/functions. pdf.
  • [36] Ollagnier, J. M. (2007). Ergodic theory and statistical mechanics (Vol. 1115): Springer.
  • [37] Osman, I. H., & Kelly, J. P. (1997). Meta-heuristics theory and applications. Journal of the Operational Research Society, 48(6), 657-657.
  • [38] Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
  • [39] Steenhof, K., Kochert, M. N., & Mcdonald, T. L. (1997). Interactive effects of prey and weather on golden eagle reproduction. Journal of Animal Ecology, 350-362.
  • [40] Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
  • [41] Watson, J. (2010). The golden eagle: Bloomsbury Publishing.
  • [42] Wilcoxon, F. (1992). Individual comparisons by ranking methods Breakthroughs in statistics (pp. 196-202): Springer.
  • [43] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary computation, 1(1), 67-82.
  • [44] Yang, X.-S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm MBD
Yazarlar

Salih Berkan Aydemir 0000-0003-0069-3479

Yayımlanma Tarihi 20 Mart 2022
Gönderilme Tarihi 9 Temmuz 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 34 Sayı: 1

Kaynak Göster

APA Aydemir, S. B. (2022). Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(1), 85-104. https://doi.org/10.35234/fumbd.969335
AMA Aydemir SB. Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Mart 2022;34(1):85-104. doi:10.35234/fumbd.969335
Chicago Aydemir, Salih Berkan. “Küresel Optimizasyon için Gauss Kaotik Haritası Ile Kartal Optimizasyonu”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34, sy. 1 (Mart 2022): 85-104. https://doi.org/10.35234/fumbd.969335.
EndNote Aydemir SB (01 Mart 2022) Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34 1 85–104.
IEEE S. B. Aydemir, “Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 34, sy. 1, ss. 85–104, 2022, doi: 10.35234/fumbd.969335.
ISNAD Aydemir, Salih Berkan. “Küresel Optimizasyon için Gauss Kaotik Haritası Ile Kartal Optimizasyonu”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34/1 (Mart 2022), 85-104. https://doi.org/10.35234/fumbd.969335.
JAMA Aydemir SB. Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34:85–104.
MLA Aydemir, Salih Berkan. “Küresel Optimizasyon için Gauss Kaotik Haritası Ile Kartal Optimizasyonu”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 34, sy. 1, 2022, ss. 85-104, doi:10.35234/fumbd.969335.
Vancouver Aydemir SB. Küresel Optimizasyon için Gauss Kaotik Haritası ile Kartal Optimizasyonu. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34(1):85-104.

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