A Hybrid DE - HS Algorithm with Randomized Parameters

Year 2018, , 119 - 124, 30.11.2018

Ezgi Deniz Ülker

https://doi.org/10.17714/gumusfenbil.427696

Abstract

The evolutionary algorithms and their hybrid methods
are quite efficient and accurate in terms of solution quality of optimization.
In this study, a new hybrid algorithm is generated by merging Differential
Evolution (DE) and Harmony Search Optimization (HS) algorithms which is called
DES. The core steps of the algorithms are used without any modifications, but
the main control parameters which directly affect the performance are
randomized. The experimental study is done by comparing DE, HS and their hybrid
method DES. According to the results, it is found that DES algorithm has
improved the performances of original algorithms for the selected test
problems.

Keywords

Differential evolution, Evolutionary algorithms, Harmony search, Hybridization, Random parameters

Rastgele Değişkenli Melez DG-HA Algoritması

Abstract

Evrimsel algoritmalar ve onları
kullanarak yaratılan melez algoritmalar optimizasyon problemlerini çözmede
etkili ve doğru sonuçlar üretirler. Bu çalışmada, Diferansiyel Gelişim (DG)
algoritması ve Harmoni Arama (HA) algoritması birleştirilerek yeni bir melez
algoritma oluşturulmuştur. Birleştirilen algoritmaların ana basamakları
herhangi bir performans yükseltme yapılmadan kullanılmıştır, ancak performans
üzerinde doğrudan etkisi olduğu bilinen ana kontrol değişken değerleri için
rastgele seçim yapılmıştır. Deneysel çalışma, birleştirilen DG ve HA
algoritmaları ile onların oluşturduğu DES algoritması arasında yapılmıştır.
Elde edilen sonuçlara göre melez algoritma DES, diğer iki algoritmaya göre daha
iyi bir performans göstermiştir.

Keywords

Diferansiyel gelişim, Evrimsel algoritmalar, Harmoni arama, Melezleme, Rastgele değişkenler

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