Karşıt Tabanlı Öğrenme İle Geliştirilmiş Yapay Denizanası Arama Algoritması

Year 2022, Issue: 44, 27 - 34, 31.12.2022

Gülnur Yıldızdan

https://doi.org/10.31590/ejosat.1219071

Abstract

Bu çalışmada denizanalarının okyanustaki yiyecek arama davranışının modellenmesi ile oluşturulan yapay denizanası arama algoritmasının (JS) performansını geliştirmek amacıyla yeni gelişmiş bir algoritma önerilmiştir. Bunun için JS’ye karşıt tabanlı öğrenme yaklaşımı dahil edilerek popülasyondaki bireylerin arama uzayına daha doğru şekilde dağıtılması sağlanmıştır. Geliştirilmiş algoritma(KJS), standart kıyaslama fonksiyonları üzerinde 10,30,50,100,500 ve 1000 boyut için test edilmiştir. Elde edilen sonuçlar JS ve literatürdeki algoritmalarla karşılaştırılmış, istatistik testler ile yorumlanmıştır. Sonuçlar değerlendirildiğinde önerilen KJS algoritmasının başarılı ve kabul edilebilir sonuçlar ürettiği tespit edilmiştir.

Keywords

Karşıt tabanlı öğrenme, Sürekli optimizasyon, Yapay denizanası arama algoritması., Opposition-based learning, Continuous optimization, Artificial jellyfish search algorithm.

Artificial Jellyfish Search Algorithm Developed With Opposition-Based Learning

Abstract

In this study, a newly developed algorithm was proposed to improve the performance of the artificial jellyfish search algorithm (JS), which is created by modeling the foraging behavior of jellyfish in the ocean. For this, an oppositional-based learning approach was included in JS to provide a more accurate distribution of individuals in the population to the search space. The developed algorithm (KJS) was tested on standard benchmark functions for 10,30,50,100,500 and 1000 dimensions. The obtained results were compared with JS and algorithms in the literature and interpreted with statistical tests. When the results were evaluated, it was determined that the proposed KJS algorithm produced successful and acceptable results.

Keywords

Opposition-based learning, Continuous optimization, Artificial jellyfish search algorithm.

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