A Hybrid DE - HS Algorithm with Randomized Parameters
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.
Anahtar Kelimeler:
Differential evolution, Evolutionary algorithms, Harmony search, Hybridization, Random parameters
Rastgele Değişkenli Melez DG-HA Algoritması
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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- Yayın Aralığı: Yılda 4 Sayı
- Başlangıç: 2011
- Yayıncı: GÜMÜŞHANE ÜNİVERSİTESİ