Performance Enhancement of Distribution Function Based Monarch Butterfly Optimization Algorithm with Optimization to Optimization Approach

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 100 - 108, 20.10.2021

Mehmet Akpamukçu Abdullah Ateş

https://doi.org/10.53070/bbd.990245

Abstract

In this study, the parameters of the distribution functions were adjusted with the optimization to optimization approach to improve the performance of the distribution function-based monarch butterfly optimization algorithm (MBO). For this, the random number generation processes, which greatly affect the flow of stochastic algorithms, were examined and the effect of distribution functions on these processes was determined. Then, the importance of parameter selection in the operation of distribution functions has been determined. It has been seen that the distribution function will be more effective with appropriate parameter selections. At this point, the distribution functions that can be used in the random number generation in the main target algorithm were tried to be determined with appropriate parameters with an upper auxiliary optimization algorithm. In conclusion; With the approach of optimization to optimization, the performance of the target algorithm has been tried to be increased and concrete results are presented in comparison with the tests made on the most used benchmark functions in the literature.

Keywords

optimization, distribution functions, stochastics algorithms, optimization to optimization

Optimizasyonun Optimizasyonu Yaklaşımıyla Dağılım Fonksiyonu Tabanlı Kral Kelebeği Optimizasyon Algoritmasının Performansının Artırılması

Abstract

Bu çalışmada, dağılım fonksiyonu tabanlı kral kelebek optimizasyon algoritmasının (KKO) performansını iyileştirmek için optimizasyonun optimizasyonu yaklaşımıyla dağılım fonksiyonlarının parametreleri ayarlanmıştır. Bunun için stokastik algoritmaların akışını büyük ölçüde etkileyen rastgele sayı üretme süreçleri incelenmiş ve dağılım fonksiyonlarının bu süreçlere etkisi belirlenmiştir. Daha sonra dağılım fonksiyonlarının işleyişinde parametre seçiminin önemi belirlenmiştir. Uygun parametre seçimleri ile dağılım fonksiyonunun daha etkin olacağı görülmüştür. Bu noktada ana hedef algoritmada rastgele sayı üretiminde kullanılabilecek uygun parametreli dağılım fonksiyonları, bir üst yardımcı optimizasyon algoritması ile belirlenmeye çalışılmıştır. Sonuç olarak; optimizasyonun optimizasyonu yaklaşımı ile hedef algoritmanın performansı artırılmaya çalışılmış ve literatürde en çok kullanılan benchmark fonksiyonları üzerinde yapılan testler ile karşılaştırmalı olarak somut sonuçlar sunulmuştur.

Keywords

Optimizasyon, dağılım fonksiyonları, stokastik algoritmalar, optimizasyonun optimizasyonu

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