ADAPTİF KURBAĞA SIÇRAMA ALGORİTMASININ OPERATÖRÜ İLE GELİŞTİRİLMİŞ YAPAY ATOM ALGORİTMASI

Year 2022, , 366 - 383, 31.08.2022

Ayşe Erdoğan Yıldırım

https://doi.org/10.54365/adyumbd.1080995

Abstract

Yapay Atom Algoritması, doğadan ilham alınarak geliştirilmiş bir optimizasyon tekniğidir. Bu algoritma önceki çalışmalarda hem sürekli problemler hem de ayrık problemler için kullanılmıştır. Bu çalışmada bu algoritmanın başarısını artıracak bir düzenleme öngörülmüştür. Bu amaçla, Yapay Atom Algoritmasının iyonik bağ işlevi, Adaptif Kurbağa Sıçrama Algoritmasının algoritmik bir adımından yararlanılarak geliştirilmiştir. Güncellemeler sonucunda iyonik bağ operatörü için arama alanı daraltılmıştır. Böylece her iterasyonda çözümden uzaklaşma durumu önlenmiştir. Geliştirilmiş Yapay Atom Algoritmasının başarısı, kıyaslama fonksiyonları ile test edilmiştir. Önerilen yöntem için deneysel sonuçlar karşılaştırmalı olarak yorumlanmıştır.

Keywords

Yapay atom algoritması, Geliştirilmiş yapay atom algoritması, Adaptif kurbağa sıçrama algoritması, Optimizasyon, Kıyaslama problemleri

AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM

Abstract

Artificial Atom Algorithm is an optimization technique that developed inspired by nature. This algorithm used for both continues problems and discrete problems in previous studies. In this study, an arrangement that would increase the success of this algorithm was envisaged. For this purpose, the ionic bond function of Artificial Atom Algorithm has been improved benefiting an algorithmic step of Shuffled Frog Leaping Algorithm. As a result of the updates, the search space was narrowed for the ionic bond operator. Thus, the state of getting away from the solution in each iteration was prevented. The success of Improved Artificial Atom Algorithm was tested with benchmark functions. Experimental results for the proposed method were interpreted comparatively.

Keywords

Artificial atom algorithm, Improved artificial atom algorithm, Shuffled frog leaping algorithm, Optimization, Benchmark problems

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