Küresel Optimizasyon Problemlerinde Balçık Kalıp Algoritması ve Hibrit Balçık Kalıp Algoritmalarının Performansının İncelenmesi

Yıl 2022, Cilt: 13 Sayı: 4, 661 - 671, 03.01.2023

Osman Altay , Elif Varol Altay

https://doi.org/10.24012/dumf.1177288

Cited By: 1

Öz

Balçık kalıp algoritması (BKA), son zamanlarda önerilen nispeten yeni bir metasezgisel tekniktir. Yeni önerilen algoritmaların performansı optimizasyon problemlerinde tatmin edici sonuçlar verirken, yakın zamanda önerilen bir algoritmanın farklı algoritmaların bileşenleri ile birleştirilmesi BKA'ların performansını iyileştirmektedir. Son yıllarda, BKA'nın farklı algoritmalarla birleştirildiği lider SMA (LBKA) ve denge optimize edici SMA (DBKA) yöntemleri önerilmiştir. Önerilen iki yöntemin farklı problemlerde BKA'ya göre avantajları gösterilmiştir. Bu çalışmada, BKA'nın yavaş yakınsama hızı ve yerel optimum gibi dezavantajlarını ortadan kaldırmak için son yıllarda önerilen LBKA ve DBKA yöntemleri ile birlikte CEC'20 test fonksiyonlarının performansları araştırılmıştır. Elde edilen sonuçlar istatistiksel olarak analiz edilmiş ve çalışmada ayrıntılı olarak verilmiştir.

Anahtar Kelimeler

Balçık kalıp algoritması, Lider balçık kalıp algoritması, Denge optimize edici balçık kalıp algoritması, CEC'20 fonksiyonları

Investigation of Slime Mould Algorithm and Hybrid Slime Mould Algorithms' Performance in Global Optimization Problems

Öz

The Slime mould algorithm (SMA) is a relatively new metaheuristic technique that was just presented. While the performance of the newly proposed algorithms gives satisfactory results in optimization problems, combining a recently proposed algorithm with the components of different algorithms improves the performance of SMAs. In recent years, leader SMA (LSMA) and equilibrium optimizer SMA (ESMA) methods, in which SMA is combined with different algorithms, have been proposed. The advantages of the two proposed methods over SMA in different problems are shown. In this study, in order to eliminate the disadvantages of SMA, such as slow convergence rate and local optimum, the performances of the CEC2020 test functions were investigated together with the LSMA and ESMA methods proposed in recent years. The results obtained are statistically analyzed and given in detail in the study.

Anahtar Kelimeler

Slime mould algorithm, leader SMA, equilibrium optimizer SMA, CEC2020, Slime mould algorithm, leader SMA, equilibrium optimizer SMA, CEC2020

Kaynakça

• Sayed, G.I., Khoriba, G., Haggag, M.H.: A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl. Intell. 48, 3462–3481 (2018). https://doi.org/10.1007/s10489-018-1158-6.
• Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007.
• Faramarzi, A., Heidarinejad, M., Stephens, B.,Mirjalili, S.: Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Syst. 191, 105190 (2020). https://doi.org/10.1016/j.knosys.2019.105190.
• Hashim, F.A., Hussain, K., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W.: Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl. Intell. 51, 1531–1551 (2021). https://doi.org/10.1007/s10489-020-01893-z.
• Dhiman, G., Kaur, A.: Spotted Hyena Optimizer for Solving Engineering Design Problems. Proc. - 2017 Int. Conf. Mach. Learn. Data Sci. MLDS 2017. 2018-Janua, 114–119 (2018). https://doi.org/10.1109/MLDS.2017.5.
• Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-qaness, M.A.A., Gandomi, A.H.: Aquila Optimizer: A novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157, 107250 (2021). https://doi.org/10.1016/j.cie.2021.107250.
• Li, S., Chen, H., Wang, M., Heidari, A.A., Mirjalili, S.: Slime mould algorithm: A new method for stochastic optimization. Futur. Gener. Comput. Syst. 111, 300–323 (2020). https://doi.org/10.1016/j.future.2020.03.055.
• Altay, E.V., Ncelenmes, İ.İ.: INVESTIGATION OF THE PERFORMANCE OF METAHEURISTIC OPTIMIZATION ALGORITHMS USED IN SOLVING REAL-WORLD ENGINEERING DESIGN PROBLEMS. 6, (2022).
• Fagan, F., Vuuren, J.H. Van: A unification of the prevalent views on exploitation, exploration, intensification and diversification. Int. J. Metaheuristics. 2, 294 (2013). https://doi.org/10.1504/ijmheur.2013.056407.
• Thangaraj, R., Pant, M., Abraham, A., Bouvry, P.: Particle swarm optimization: Hybridization perspectives and experimental illustrations. Appl. Math. Comput. 217, 5208–5226 (2011). https://doi.org/10.1016/j.amc.2010.12.053.
• Naik, M.K., Panda, R., Abraham, A.: Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm. J. King Saud Univ. - Comput. Inf. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.10.030.
• Naik, M.K., Panda, R., Abraham, A.: An entropy minimization based multilevel colour thresholding technique for analysis of breast thermograms using equilibrium slime mould algorithm. Appl. Soft Comput. 113, 107955 (2021). https://doi.org/10.1016/j.asoc.2021.107955.
• Altay, O.: Chaotic slime mould optimization algorithm for global optimization. Springer Netherlands (2022). https://doi.org/10.1007/s10462-021-10100-5.
• A.W., Hadi, A.A., Mohamed, A.K., Awad, N.H.: Evaluating the Performance of Adaptive GainingSharing Knowledge Based Algorithm on CEC 2020 Benchmark Problems. 2020 IEEE Congr. Evol. Comput. CEC 2020 - Conf. Proc. (2020). https://doi.org/10.1109/CEC48606.2020.9185901.
• Varol Altay, E., Altay, O.: Güncel metasezgisel optimizasyon algoritmalarının CEC2020 test fonksiyonları ile karşılaştırılması. DÜMF Mühendislik Derg. 5, 729–741