Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

Year 2021, Volume: 9 Issue: 6 - ICAIAME 2021, 40 - 54, 31.12.2021

Çağrı Suiçmez Hamdi Kahraman Cemal Yılmaz Mehmet Fatih Işık Enes Cengiz

https://doi.org/10.29130/dubited.1016209

Cited By: 3

Abstract

Bu çalışmada güncel bir Meta-Heuristic Search algoritması olan Balçık-Küfü Algoritması (SMA) performansının iyileştirmesi yapılmaktadır. SMA algoritmasında arama süreci yaşam döngüsü sürecini daha etkili bir şekilde modelleyebilmek için arama sürecine rehberlik eden çözüm adayları uzaklık-uygunluk dengesi (fitness-distance balance, FDB) yöntemi kullanılarak belirlenmiştir. Her ne kadar SMA algoritmasının performansı kabul görse de uygulanan FDB yöntemi sayesinde geliştirilen FDB-SMA algoritmasının performansının çok daha iyi olduğu görülmektedir. Geliştirilen FDB-SMA algoritmasının performansını test etmek için güncel benchmark sorunları olan CEC 2020 kullanılmıştır. CEC 2020'den alınan 10 farklı kısıtsız kıyaslama problemi 30-50-100 boyutlarında düzenlenerek tasarlanmıştır. Deneysel çalışmalar tasarlanan kıyaslama problemleri kullanılarak gerçekleştirilmiş ve Friedman ve Wilcoxon istatistiksel test yöntemleri ile analiz edilmiştir. Analiz sonuçlarına göre FDB-SMA varyasyonlarının tüm deneysel çalışmalarda temel algoritmaya (SMA) göre daha üstün bir performans gösterdiği görülmüştür.

Keywords

Meta-sezgisel arama, Slime Mould algoritması, uygunluk-mesafe dengesi (FDB), kıyaslama problemleri

Improved Slime-Mould-Algorithm with Fitness Distance Balance-based Guiding Mechanism for Global Optimization Problems

Abstract

In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.

Keywords

Meta-heuristic search, slime mould algorithm, fitness-distance balance (FDB), benchmark problems

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