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Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması

Yıl 2022, , 286 - 301, 31.12.2022
https://doi.org/10.29132/ijpas.1070287

Öz

Metasezgisel yöntemler genellikle doğadan ilham alınarak oluşturulmuş algoritmalardır. Bu yöntemler özellikle karmaşık problemlerin çözümünde oldukça başarılı sonuçlar üretmektedir. Önerilen yöntemlerin performansları, uygulanan probleme göre değişiklik göstermektedir. Bu çalışmada son dönemlerde ortaya çıkmış ve popüler olan Harris Şahin Optimizasyon Algoritması, Serçe Arama Algoritması, Çoklu Evren Optimizasyonu, Deniz Avcıları Algoritması ve Coot Optimizasyon Algoritması detaylı bir şekilde incelenmiştir. Bu algoritmalar 23 standart kalite testi fonksiyonlarında analiz edilmiştir. Analiz edilen fonksiyonlar tek modlu kalite testi fonksiyonları, çok modlu kalite testi fonksiyonları, karmaşık boyutlu çok modlu kalite testi fonksiyonlarından oluşmaktadır.

Destekleyen Kurum

yok

Proje Numarası

yok

Teşekkür

yok

Kaynakça

  • Altay, E. V. ve Alatas, B. (2020a). Bird swarm algorithms with chaotic mapping. Artificial Intelligence Review, 53(2), 1373-1414.
  • Altay, E. V. ve Alatas, B. (2021). Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining. Information Sciences, 554, 198-221.
  • Altay, E. V. ve Alatas, B. (2020b). Randomness as source for inspiring solution search methods: Music based approaches. Physica A: Statistical Mechanics and its Applications, 537, 122650.
  • Altay, E. V. ve Alatas, B. (2019). Performance comparisons of socially inspired metaheuristic algorithms on unconstrained global optimization. In Advances in Computer Communication and Computational Sciences (pp. 163-175). Springer, Singapore.
  • Altay, E. V. ve Altay, O. (2021). Güncel metasezgisel optimizasyon algoritmalarının CEC2020 test fonksiyonları ile karşılaştırılması, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12 (5), pp. 729-741.
  • Altay, O. (2021). Chaotic slime mould optimization algorithm for global optimization. Artificial Intelligence Review, 1-62.
  • Bonabeau, E., Dorigo, M. ve Theraulaz, G. Swarm intelligence: from natural toartificial systems: OUP USA; 1999.
  • Blum, C. ve Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35 (3), 268–308.
  • Dhiman, G. ve Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50.
  • Dhiman, G. ve Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169-196.
  • Dorigo, M., Birattari, M. ve Stutzle, T. (2006). Ant colony optimization. Comput Intell Magaz, IEEE, 1,28–39.
  • Faramarzi, A., Heidarinejad, M., Mirjalili, S. ve Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377.
  • Geem, Z. W., Kim, J. H. ve Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. simulation, 76(2), 60-68.
  • Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. ve Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
  • Ho, Y. C. ve Pepyne, D. L. (2002). Simple explanation of the no-free-lunch theorem and its implications. Journal of optimization theory and applications, 115(3), 549-570.
  • Karcı, A. (2012). A new metaheuristic algorithm based chemical process: Atom Algorithm (p:85). Proc. 1st International Eurasian Conference on Mathematical Sciences and Applications, September 03-07, Pristina, Kosova.
  • Kashan, A. H. (2009). League Championship Algorithm: A new algorithm for numerical function optimization. In SoCPaR, 43-48.
  • Kaveh, A. ve Bakhshpoori T. (2016). Water evaporation optimization: A novel physically inspired optimization algorithm. Computers & Structures, 167, 69-85.
  • Kennedy, J. ve Eberhart, R. (1995). Particle swarm optimization, in Neural Networks, In: Proceedings, IEEE international conference on. 1942–1948.
  • Kızıloluk, S. ve Can, Ü. (2021). Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences, 7(1), 100-112.
  • Kripka, M. ve Kripka, R. M. L. (2008). Big crunch optimization method. In International conference on engineering optimization. Brazil, 1-5.
  • Labbi, Y., Attous, D. B., Gabbar, H. A., Mahdad, B. ve Zidan, A. (2016). A new rooted tree optimization algorithm for economic dispatch with valve-point effect. Int J Electr Power Energy Syst 79, 298–311.
  • Naruei, I. ve Keynia, F. (2021). A New Optimization Method Based on Coot Bird Natural Life Model. Expert Systems with Applications, 115352.
  • Mirjalili, S., Mirjalili, S. M. ve Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
  • Mirjalili, S., Mirjalili, S. M. ve Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • Mirjalili S. (2016). SCA: A Sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
  • Shi, Y. (2011). Brain storm optimization algorithm. In International Conference in Swarm Intelligence. 303-309, Springer Berlin Heidelberg.
  • Weise, T. (2011). Global optimization algorithms-theory and application (third edition) Online E-Book http://www.it-weise.de/projects/bookNew.pdf.
  • Xing, B. ve Gao, W. J. (2014). Central force optimization algorithm. In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. 333-337, Springer International Publishing.
  • Xue, J. ve Shen, B. (2020). A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 8(1), 22-34.
  • Yang, X. S. (2010). Engineering optimization: An introduction with metaheuristic applications. Hoboken new jersey: John Wiley & Sons.

Comparison of Current Metaheuristic Methods in Standard Benchmark Functions

Yıl 2022, , 286 - 301, 31.12.2022
https://doi.org/10.29132/ijpas.1070287

Öz

Metaheuristic methods are generally algorithms inspired by nature. These methods produce very successful results especially in solving complex problems. The performances of the proposed methods vary according to the applied problem. In this study, the recently emerged and popular Harris Hawk Optimization Algorithm, Sparrow Search Algorithm, Multi-verse Optimization, Marine Predators Algorithm and Coot Optimization Algorithm are examined in detail. These algorithms were analyzed in 23 standard benchmark functions. The analyzed functions consist of unimodal benchmark functions, multimodal benchmark functions, fixed dimension multimodal benchmark functions.

Proje Numarası

yok

Kaynakça

  • Altay, E. V. ve Alatas, B. (2020a). Bird swarm algorithms with chaotic mapping. Artificial Intelligence Review, 53(2), 1373-1414.
  • Altay, E. V. ve Alatas, B. (2021). Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining. Information Sciences, 554, 198-221.
  • Altay, E. V. ve Alatas, B. (2020b). Randomness as source for inspiring solution search methods: Music based approaches. Physica A: Statistical Mechanics and its Applications, 537, 122650.
  • Altay, E. V. ve Alatas, B. (2019). Performance comparisons of socially inspired metaheuristic algorithms on unconstrained global optimization. In Advances in Computer Communication and Computational Sciences (pp. 163-175). Springer, Singapore.
  • Altay, E. V. ve Altay, O. (2021). Güncel metasezgisel optimizasyon algoritmalarının CEC2020 test fonksiyonları ile karşılaştırılması, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12 (5), pp. 729-741.
  • Altay, O. (2021). Chaotic slime mould optimization algorithm for global optimization. Artificial Intelligence Review, 1-62.
  • Bonabeau, E., Dorigo, M. ve Theraulaz, G. Swarm intelligence: from natural toartificial systems: OUP USA; 1999.
  • Blum, C. ve Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35 (3), 268–308.
  • Dhiman, G. ve Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50.
  • Dhiman, G. ve Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169-196.
  • Dorigo, M., Birattari, M. ve Stutzle, T. (2006). Ant colony optimization. Comput Intell Magaz, IEEE, 1,28–39.
  • Faramarzi, A., Heidarinejad, M., Mirjalili, S. ve Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377.
  • Geem, Z. W., Kim, J. H. ve Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. simulation, 76(2), 60-68.
  • Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. ve Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
  • Ho, Y. C. ve Pepyne, D. L. (2002). Simple explanation of the no-free-lunch theorem and its implications. Journal of optimization theory and applications, 115(3), 549-570.
  • Karcı, A. (2012). A new metaheuristic algorithm based chemical process: Atom Algorithm (p:85). Proc. 1st International Eurasian Conference on Mathematical Sciences and Applications, September 03-07, Pristina, Kosova.
  • Kashan, A. H. (2009). League Championship Algorithm: A new algorithm for numerical function optimization. In SoCPaR, 43-48.
  • Kaveh, A. ve Bakhshpoori T. (2016). Water evaporation optimization: A novel physically inspired optimization algorithm. Computers & Structures, 167, 69-85.
  • Kennedy, J. ve Eberhart, R. (1995). Particle swarm optimization, in Neural Networks, In: Proceedings, IEEE international conference on. 1942–1948.
  • Kızıloluk, S. ve Can, Ü. (2021). Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences, 7(1), 100-112.
  • Kripka, M. ve Kripka, R. M. L. (2008). Big crunch optimization method. In International conference on engineering optimization. Brazil, 1-5.
  • Labbi, Y., Attous, D. B., Gabbar, H. A., Mahdad, B. ve Zidan, A. (2016). A new rooted tree optimization algorithm for economic dispatch with valve-point effect. Int J Electr Power Energy Syst 79, 298–311.
  • Naruei, I. ve Keynia, F. (2021). A New Optimization Method Based on Coot Bird Natural Life Model. Expert Systems with Applications, 115352.
  • Mirjalili, S., Mirjalili, S. M. ve Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
  • Mirjalili, S., Mirjalili, S. M. ve Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • Mirjalili S. (2016). SCA: A Sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
  • Shi, Y. (2011). Brain storm optimization algorithm. In International Conference in Swarm Intelligence. 303-309, Springer Berlin Heidelberg.
  • Weise, T. (2011). Global optimization algorithms-theory and application (third edition) Online E-Book http://www.it-weise.de/projects/bookNew.pdf.
  • Xing, B. ve Gao, W. J. (2014). Central force optimization algorithm. In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. 333-337, Springer International Publishing.
  • Xue, J. ve Shen, B. (2020). A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 8(1), 22-34.
  • Yang, X. S. (2010). Engineering optimization: An introduction with metaheuristic applications. Hoboken new jersey: John Wiley & Sons.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Osman Altay 0000-0003-3989-2432

Proje Numarası yok
Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 8 Şubat 2022
Kabul Tarihi 24 Mayıs 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Altay, O. (2022). Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması. International Journal of Pure and Applied Sciences, 8(2), 286-301. https://doi.org/10.29132/ijpas.1070287
AMA Altay O. Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması. International Journal of Pure and Applied Sciences. Aralık 2022;8(2):286-301. doi:10.29132/ijpas.1070287
Chicago Altay, Osman. “Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması”. International Journal of Pure and Applied Sciences 8, sy. 2 (Aralık 2022): 286-301. https://doi.org/10.29132/ijpas.1070287.
EndNote Altay O (01 Aralık 2022) Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması. International Journal of Pure and Applied Sciences 8 2 286–301.
IEEE O. Altay, “Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması”, International Journal of Pure and Applied Sciences, c. 8, sy. 2, ss. 286–301, 2022, doi: 10.29132/ijpas.1070287.
ISNAD Altay, Osman. “Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması”. International Journal of Pure and Applied Sciences 8/2 (Aralık 2022), 286-301. https://doi.org/10.29132/ijpas.1070287.
JAMA Altay O. Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması. International Journal of Pure and Applied Sciences. 2022;8:286–301.
MLA Altay, Osman. “Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması”. International Journal of Pure and Applied Sciences, c. 8, sy. 2, 2022, ss. 286-01, doi:10.29132/ijpas.1070287.
Vancouver Altay O. Güncel Metasezgisel Yöntemlerin Standart Kalite Testi Fonksiyonlarında Karşılaştırılması. International Journal of Pure and Applied Sciences. 2022;8(2):286-301.

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