Artırımlı Sosyal Öğrenme Tabanlı Diferansiyel Gelişim Algoritması

Bu çalışmada, literatürde yer alan optimizasyon algoritmaları arasında çok güçlü bir yere sahip olan diferansiyel gelişim algoritmasının (DE) geliştirilmesi ve iyileştirilmesi üzerine çalışılmıştır. DE’ye daha önce farklı optimizasyon algoritmalarına uygulanan ve olumlu geri dönüşler alınan artırımlı sosyal öğrenme yapısı (ISL) farklı yaklaşımlarla entegre edilerek, algoritma iyileştirilmiştir. Yapılan bu iyileştirmelerde DE, belirlenen minimum sayıda bireyle aramaya başlatılmış, belirli adımlarda farklı yaklaşımlarla popülasyona yeni bireyler eklenmiş, belirlenen maksimum popülasyon sayısında birey ekleme işlemi sonlandırılmış ve durdurma kriteri sağlanana kadar bu popülasyon sayısıyla aramaya devam edilmiştir. DE’nin yeni bir versiyonu olarak ortaya çıkarılan iyileştirilmiş bu algoritmaya artırımlı diferansiyel gelişim algoritmasının (IDE) adı verilmiştir. Çalışmada öne çıkan diğer bir amaç ISL yapısında en iyi birey ekleme yönteminin belirlenmesidir. Bu amaçla, DE’ye birey ekleme işlemi beş farklı yaklaşımla yapılmıştır. DE ve bu çalışmada geliştirilen IDE algoritmalarıyla, 13 adet, 30 boyutlu unimodal ve multimodal test fonksiyonlarının çözümleri yapılmıştır. Elde edilen sayısal sonuçlar, grafikler ve istatistiki analizler incelenerek, değerlendirmeler yapılmıştır.

Differential Evolution Algorithm with Incremental Social Learning

In this study, the differential evolution algorithm (DE), which has a very strong place among the optimization algorithms in literature, has been tried to be improved and bettered. The algorithm has been bettered by integrating incremental social learning (ISL) structure, which was applied to different optimization algorithms previously with positive feedbacks, into DE. In this betterment, DE has been initiated to search with a number of determined individuals, new individuals have been added to the population with different approaches in certain levels, the process of adding individuals has been ended at the maximum population number determined and the search has been continued with this population number until the stopping criterion has been provided. This new bettered algorithm which has been revealed as a new version of DE has been called Incremental differential evolution algorithm (IDE). Another purpose that comes into prominence in the study is to determine the best method to add individuals in ISL structure. For this purpose, five different approaches have been used in the operation of adding individuals to DE. A set of 13 unimodal and multimodal test functions defined on a 30-dimensional space have been solved with DE and IDE algorithms improved in this study. Evaluations have been made by examining the obtained numerical results, graphics and statistical analyses.

Keywords:

Differential Evolution Algorithm (DE), Incremental social learnings (ISL), Unimodal and multimodal benchmark functions Optimization,

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