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

Year 2022, Volume: 13 Issue: 4, 661 - 671, 03.01.2023

Osman Altay , Elif Varol Altay

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

Cited By: 1

Abstract

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.

Keywords

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

Abstract

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.

Keywords

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

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