Nümerik Fonksiyonların Optimizasyonu için Karşıt Tabanlı Yeni Bir Meta-Sezgisel Algoritma
Bu çalışmada literatürde meta-sezgisel algoritmaların performanslarını artırmaya yönelik yaklaşımlardan biri olan zıt konumlu öğrenme kavramı (OBL), yerçekimsel arama algoritmasına (GSA) iki farklı şekilde uygulanmıştır. Birinci yaklaşım da (ObGSA-1), ilk popülasyonunun oluşturulmasında ajanların yarısı rastgele atanırken, diğer yarısı bu ajanların simetrisine konumlandırılmıştır. İkinci yaklaşımda (ObGSA-2) ise ilk popülasyonda, rastgele olarak oluşturulan bütün ajanların zıt konumları belirlenmiş ve uygunluk değeri daha yüksek olan ajanlarla ilk popülasyon oluşturulmuştur. Bu yaklaşımlarla performans ve kararlılık açısından algoritma iyileştirilmiştir. Ortaya çıkan bu yeni algoritmaya zıt konumlu yerçekimsel arama algoritması (Opposite Based Gravitational Search Algorithm-ObGSA) adı verilmiştir. Performans analizi için ObGSA üç farklı yapıdaki test fonksiyonlarına uygulanmıştır. Bu sonuçlara geliştirilen her iki yaklaşımda (ObGSA-1, ObGSA-2), GSA’ya göre daha iyi sonuçlar vermiştir. İki yaklaşım kendi aralarında değerlendirildiğinde ise ObSA-2 yaklaşımının, ObGSA-1 yaklaşımına göre daha iyi değerler yakaladığı ve daha kararlı bir yapı olduğu sonucuna varılmıştır.
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