Çift-Girişim Tabanlı İyileştirme Algoritmasının Sayısal İyileştirme Fonksiyonları Üzerinde Performans Analizi

Yıl 2023, , 545 - 552, 28.07.2023

Mehtap Köse Ulukök

https://doi.org/10.21605/cukurovaumfd.1334219

Öz

Sayısal iyileştirme, mühendislik alanında en çok uğraşılan problemlerden biridir. Bu çalışmada, son zamanlarda geliştirilen Çift-Girişim Tabanlı İyileştirme Algoritması’nın (Bi-Attempted Based Optimization Algorithm) (ABaOA) arama yakınsama kabiliyeti yirmi iyi bilinen referans fonksiyonu üzerinde test edilmiştir. Elde edilen sonuçlar Genetik Algoritma (GA) ve Temel İyileştirme Algoritması (Based Optimization Algoritması) (BaOA) ile karşılaştırılmıştır. ABaOA, tüm yinelemeler boyunca iki sabit adım boyutlu çoğaltma parametresi ve iki işlem operatörü kullanan nüfus tabanlı bir Evrimsel Algoritma’dır. Evrimsel algoritmalar arama alanı boyunca global optimuma hızlı bir şekilde yaklaşır ve uygulanabilir bir çözümü garanti ederler. Deneysel sonuçlar ABaOA'nın hem BAOA'ya hem de GA'ya göre global optimuma daha hızlı yaklaştığını açıkça göstermiştir.

Anahtar Kelimeler

Bilgisayar zekası, Evrimsel algoritmalar, İyileştirme problemleri

Performance Analysis of Bi-Attempted Based Optimization Algorithm on Numerical Optimization Functions

Öz

Numerical optimization is one of the most challenging problem in engineering field. In this study, a recently developed Bi-Attempted Based Optimization Algorithm (ABaOA) is tested on twenty well-known benchmark functions to find its search convergence capability. Obtained results are compared with the Genetic Algorithm (GA) and the Base Optimization Algorithm (BaOA). ABaOA is a population-based Evolutionary Algorithm that uses two fixed step-size displacement parameter and two arithmetic reproduction operators throughout all the iterations. Evolutionary algorithms converge to the global optimum throughout the search space quickly, and they guarantee a feasible solution. The experimental results clearly showed that the ABaOA reaches the global optimum faster than the BaOA and the GA.

Anahtar Kelimeler

Computational intelligence, Evolutionary algorithms, Optimization problems

Kaynakça

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