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Comparison of Current Metaheuristic Optimization Algorithms by Using Benchmark Functions

Yıl 2021, Cilt: 7 Sayı: 1, 100 - 112, 30.06.2021
https://doi.org/10.29132/ijpas.855869

Öz

There are dozens of metaheuristic optimization methods inspired by the swarm behaviors of creatures in nature, plants, human-specific phenomena, events in scientific fields such as physics, mathematics, biology and chemistry. These methods are successful in certain problems but not in all problems. Therefore, new metaheuristic methods are suggested by researchers every day. In this study, for the first time, six up-to-date metaheuristic optimization algorithms, namely Jellyfish Search Optimizer, Carnivorous Plant Algorithm, Giza Pyramids Construction Algorithm, Gradient Based Optimizer, Student Based Psychology Optimization and Tunicate Swarm Optimization, were compared by using 10 mathematical benchmark functions with 10, 30 and 50 dimensions. According to the results obtained, Student Based Psychology Optimization gave the best results in 7 out of 10 benchmark functions. It was observed that Gradient Based Optimizer gave the best results in 4 benchmark functions. Carnivorous Plant Algorithm and Tunicate Swarm Optimization showed the worst performance. In order to compare in terms of time, algorithms were run at 1000 iterations in 50-dimensional benchmark functions and when the average run times obtained are examined, it is seen that Jellyfish Search Optimizer and Tunicate Swarm Optimization are the fastest running algorithms. Carnivorous Plant Algorithm and Student Based Psychology Optimization were the slowest running algorithms.

Kaynakça

  • Ahmadianfar, I., Bozorg-Haddad, O., Chu, X., 2020. Gradient-based optimizer: A new Metaheuristic optimization algorithm. Information Sciences, 540:131-159.
  • Alatas, B., 2012. A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Systems with Applications, 39(12):11080-11088.
  • Alatas, B., Akin, E., Ozer, A. B., 2009. Chaos embedded particle swarm optimization algorithms, Chaos, Solitons and Fractals, 40(4):1715–1734.
  • Ashrafi, S. M., Dariane, A. B., 2011. A novel and effective algorithm for numerical optimization: melody search (MS). 11th International Conference on Hybrid Intelligent Systems (HIS) (pp. 109-114). IEEE.
  • Birbil, S.I., Fang, S.C., 2003. An Electromagnetism-like Mechanism for Global Optimization. Journal of Global Optimization, 25:263-282.
  • Borji, A., Hamidi, M., 2009. A new approach to global optimization motivated by parliamentary political competitions. International Journal of Innovative Computing, Information and Control, 5(6):1643-1653.
  • Can, Ü., Alataş, B., 2015. Bitki zekâsında yeni bir alan: kök kütlesi optimizasyonu. Türk Doğa Ve Fen Dergisi, 8.
  • Chou, J. S., Truong, D. N., 2021. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389:125535.
  • Das, B., Mukherjee, V., Das, D., 2020. Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering Software, 146:102804.
  • Gao, S., de Silva, C. W., 2018. Estimation distribution algorithms on constrained optimization problems. Applied Mathematics and Computation, 339:323-345.
  • Harifi, S., Mohammadzadeh, J., Khalilian, M., Ebrahimnejad, S., 2020. Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization. Evolutionary Intelligence, 1-19.
  • Holland, J. H., 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
  • Jamil, M., Yang, X. S., 2013. A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2):150-194.
  • Karaboga, D., Akay, B., 2009. A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 214(1):108-132.
  • Kashan, A. H., 2014. League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships. Applied Soft Computing, 16:171-200.
  • Kaur, S., Awasthi, L. K., Sangal, A. L., Dhiman, G., 2020. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90:103541.
  • Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.
  • Kızıloluk, S., Özer, A. B., 2016. Melez elektromanyetizma benzeri-parçacık sürü optimizasyon algoritması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 7(3):515-526.
  • Lee, K. S., Geem, Z. W., 2005. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer methods in applied mechanics and engineering, 194:3902-3933.
  • Mirjalili, S., Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software, 95:51-67.
  • Ong, K. M., Ong, P., Sia, C. K., 2021. A carnivorous plant algorithm for solving global optimization problems. Applied Soft Computing, 98:106833.
  • Osaba, E., Diaz, F., Onieva, E., 2014. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, 41(1):145-166.
  • Qi, X., Zhu, Y., Chen, H., Zhang, D., Niu, B., 2013. An idea based on plant root growth for numerical optimization. In International Conference on Intelligent Computing (pp. 571-578), Springer, Berlin, Heidelberg.
  • Rao, R. V., Savsani, V. J., Vakharia, D. P., 2012. Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1):1-15.
  • Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., 2009. GSA: a gravitational search algorithm. Information sciences, 179(13):2232-2248.
  • Sacco, W. F., Oliveira, C. R. D., 2005. A New Stochastic Optimization Algorithm based on a Particle Collision Metaheuristic. 6th World Congresses of Structural and Multidisciplinary Optimization, Rio de Janerio, Brazil.
  • 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.
  • Xie, L., Zeng, J., Cui, Z., 2009. General framework of artificial physics optimization algorithm. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 1321-1326). IEEE.
  • Yang, X. S., Gandomi, A. H., 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering computations, 29(5):464-483.
  • Yang, X. S., 2012. Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.

Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması

Yıl 2021, Cilt: 7 Sayı: 1, 100 - 112, 30.06.2021
https://doi.org/10.29132/ijpas.855869

Öz

Doğadaki canlıların sürü davranışlarından, bitkilerden, insana özgü olgulardan, fizik, matematik, biyoloji ve kimya gibi bilimsel alanlardaki olaylardan esinlenen onlarca metasezgisel optimizasyon yöntemi mevcuttur. Bu yöntemler belirli problemlerde başarılı olmakla birlikte bütün problemlerde başarılı olamamaktadır. Bundan dolayı araştırmacılar tarafından her geçen gün yeni metasezgisel yöntemler önerilmektedir. Bu çalışmada ilk defa güncel Yapay Deniz Anası Optimizasyonu, Etçil Bitki Optimizasyonu, Giza Piramitleri İnşaatı Optimizasyonu, Gradyan Tabanlı Optimizasyon, Öğrenci Psikolojisine Dayalı Optimizasyon ve Tunik Sürüsü Optimizasyonu olmak üzere altı güncel metasezgisel optimizasyon algoritması 10 adet matematiksel kalite testi foksiyonunda 10, 30 ve 50 boyut değerleri baz alınarak ayrıntılı bir şekilde karşılaştırılmıştır. Elde edilen sonuçlara göre 10 kalite testinden 7’sinde en iyi sonuçları Öğrenci Psikolojisine Dayalı Optimizasyon vermiştir. Gradyan Tabanlı Optimizasyon’un ise 4 kalite testinde en iyi sonuçları verdiği görülmüştür. En kötü performansı ise Etçil Bitki Optimizasyonu ve Tunik Sürüsü Optimizasyonu göstermiştir. Süre bakımından karşılaştırmak üzere algoritmalar 50 boyutlu test fonksiyonlarında 1000 iterasyonda çalıştırılmış ve elde edilen ortalama çalışma süreleri incelendiğinde, Yapay Deniz Anası Optimizasyonu ve Tunik Sürüsü Optimizasyonu’nun en hızlı çalışan algoritmalar olduğu görülmektedir. Etçil Bitki Optimizasyonu ve Öğrenci Psikolojisine Dayalı Optimizasyon ise en yavaş çalışan algoritmalar olmuştur.

Kaynakça

  • Ahmadianfar, I., Bozorg-Haddad, O., Chu, X., 2020. Gradient-based optimizer: A new Metaheuristic optimization algorithm. Information Sciences, 540:131-159.
  • Alatas, B., 2012. A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Systems with Applications, 39(12):11080-11088.
  • Alatas, B., Akin, E., Ozer, A. B., 2009. Chaos embedded particle swarm optimization algorithms, Chaos, Solitons and Fractals, 40(4):1715–1734.
  • Ashrafi, S. M., Dariane, A. B., 2011. A novel and effective algorithm for numerical optimization: melody search (MS). 11th International Conference on Hybrid Intelligent Systems (HIS) (pp. 109-114). IEEE.
  • Birbil, S.I., Fang, S.C., 2003. An Electromagnetism-like Mechanism for Global Optimization. Journal of Global Optimization, 25:263-282.
  • Borji, A., Hamidi, M., 2009. A new approach to global optimization motivated by parliamentary political competitions. International Journal of Innovative Computing, Information and Control, 5(6):1643-1653.
  • Can, Ü., Alataş, B., 2015. Bitki zekâsında yeni bir alan: kök kütlesi optimizasyonu. Türk Doğa Ve Fen Dergisi, 8.
  • Chou, J. S., Truong, D. N., 2021. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389:125535.
  • Das, B., Mukherjee, V., Das, D., 2020. Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering Software, 146:102804.
  • Gao, S., de Silva, C. W., 2018. Estimation distribution algorithms on constrained optimization problems. Applied Mathematics and Computation, 339:323-345.
  • Harifi, S., Mohammadzadeh, J., Khalilian, M., Ebrahimnejad, S., 2020. Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization. Evolutionary Intelligence, 1-19.
  • Holland, J. H., 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
  • Jamil, M., Yang, X. S., 2013. A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2):150-194.
  • Karaboga, D., Akay, B., 2009. A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 214(1):108-132.
  • Kashan, A. H., 2014. League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships. Applied Soft Computing, 16:171-200.
  • Kaur, S., Awasthi, L. K., Sangal, A. L., Dhiman, G., 2020. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90:103541.
  • Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.
  • Kızıloluk, S., Özer, A. B., 2016. Melez elektromanyetizma benzeri-parçacık sürü optimizasyon algoritması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 7(3):515-526.
  • Lee, K. S., Geem, Z. W., 2005. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer methods in applied mechanics and engineering, 194:3902-3933.
  • Mirjalili, S., Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software, 95:51-67.
  • Ong, K. M., Ong, P., Sia, C. K., 2021. A carnivorous plant algorithm for solving global optimization problems. Applied Soft Computing, 98:106833.
  • Osaba, E., Diaz, F., Onieva, E., 2014. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, 41(1):145-166.
  • Qi, X., Zhu, Y., Chen, H., Zhang, D., Niu, B., 2013. An idea based on plant root growth for numerical optimization. In International Conference on Intelligent Computing (pp. 571-578), Springer, Berlin, Heidelberg.
  • Rao, R. V., Savsani, V. J., Vakharia, D. P., 2012. Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1):1-15.
  • Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., 2009. GSA: a gravitational search algorithm. Information sciences, 179(13):2232-2248.
  • Sacco, W. F., Oliveira, C. R. D., 2005. A New Stochastic Optimization Algorithm based on a Particle Collision Metaheuristic. 6th World Congresses of Structural and Multidisciplinary Optimization, Rio de Janerio, Brazil.
  • 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.
  • Xie, L., Zeng, J., Cui, Z., 2009. General framework of artificial physics optimization algorithm. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 1321-1326). IEEE.
  • Yang, X. S., Gandomi, A. H., 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering computations, 29(5):464-483.
  • Yang, X. S., 2012. Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Soner Kızıloluk 0000-0002-0381-9631

Ümit Can 0000-0002-8832-6317

Yayımlanma Tarihi 30 Haziran 2021
Gönderilme Tarihi 7 Ocak 2021
Kabul Tarihi 26 Şubat 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 7 Sayı: 1

Kaynak Göster

APA Kızıloluk, S., & Can, Ü. (2021). Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences, 7(1), 100-112. https://doi.org/10.29132/ijpas.855869
AMA Kızıloluk S, Can Ü. Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences. Haziran 2021;7(1):100-112. doi:10.29132/ijpas.855869
Chicago Kızıloluk, Soner, ve Ümit Can. “Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması”. International Journal of Pure and Applied Sciences 7, sy. 1 (Haziran 2021): 100-112. https://doi.org/10.29132/ijpas.855869.
EndNote Kızıloluk S, Can Ü (01 Haziran 2021) Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences 7 1 100–112.
IEEE S. Kızıloluk ve Ü. Can, “Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması”, International Journal of Pure and Applied Sciences, c. 7, sy. 1, ss. 100–112, 2021, doi: 10.29132/ijpas.855869.
ISNAD Kızıloluk, Soner - Can, Ümit. “Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması”. International Journal of Pure and Applied Sciences 7/1 (Haziran 2021), 100-112. https://doi.org/10.29132/ijpas.855869.
JAMA Kızıloluk S, Can Ü. Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences. 2021;7:100–112.
MLA Kızıloluk, Soner ve Ümit Can. “Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması”. International Journal of Pure and Applied Sciences, c. 7, sy. 1, 2021, ss. 100-12, doi:10.29132/ijpas.855869.
Vancouver Kızıloluk S, Can Ü. Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması. International Journal of Pure and Applied Sciences. 2021;7(1):100-12.

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