Biyoinspirasyon tabanlı derin öğrenme algoritması

Yıl 2025, Cilt: 40 Sayı: 2, 979 - 994

Mehmet Akif Çifçi , Peren Jerfi Canatalay Emrah Arslan , Samina Kausar

https://doi.org/10.17341/gazimmfd.1424002

Öz

Bu makale, biyolojik sistemler ve Derin Öğrenme (DÖ) tekniklerinden esinlenen yenilikçi bir öğrenilmiş sezgisel yöntem olan Enfeksiyona Duyarlı Yapay Zekâ Optimizasyon Modeli (SIMO) işlenmektedir. SIMO optimizasyon algoritması, Enfeksiyona Duyarlı Yapay Zekâ ile epidemiyolojik bölme modelinden ilham alarak herhangi bir zamandaki nüfusun enfeksiyona duyarlılığını, aktif enfeksiyonları ve iyileşen popülasyonu tahmin etmektedir. SIMO, arama sürecini iyileştirmek amacıyla başlatma yöntemi ve parametre ayarlama bileşenlerine DÖ metodunu entegre eder, bu sayede zeki ve otonom davranış sergileyebilmektedir. DÖ entegrasyonu, algoritmanın etkin, etkili ve güçlü arama sonuçlarına yönlendirilmesine olanak tanıyan nöral modellere dayalı başlangıç çözümleri üretmeyi kolaylaştırmaktadır. Bu yaklaşım, algoritmanın performansını üst düzey çözümler elde ederek, daha hızlı bir şekilde yakınsamasını sağlayarak, güçlülüğünü artırarak ve hesaplama gereksinimlerini azaltarak geliştirir. SIMO algoritmasının etkinliğini doğrulamak için 2017 IEEE Evrimsel Hesaplama Kongresi (CEC 2017) benchmarking fonksiyonlarından alınan iki veri seti kullanılmıştır ve deneysel sonuçlar yenilikçi algoritmalarla karşılaştırılmıştır. Detaylı karşılaştırmalar, SIMO'nun birçok benzer modeli geride bıraktığını, daha az kontrol parametresi kullanarak yüksek performanslı çözümler sunduğunu göstermektedir. Ayrıca, SIMO'nun performansı gerçek hayat problemlerine uyarlanmıştır. Sonuçlar, SIMO'ya öğrenme sürecini entegre etmenin, mevcut literatürdeki diğer optimizasyon yaklaşımlarına kıyasla üstün hassasiyet ve hesaplama verimliliği sağladığını açıkça göstermektedir.

Anahtar Kelimeler

derin öğrenme, SIRO nöral öğrenme, optimizasyon algoritmaları, mühendislik tasarım optimizasyonu, metaheuristik

Kaynakça

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