gummfd Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1300-1884 1304-4915 Gazi Üniversitesi 10.17341/gazimmfd.1424002 Deep Learning Evolutionary Computation Modelling and Simulation Derin Öğrenme Evrimsel Hesaplama Modelleme ve Simülasyon Biyoinspirasyon tabanlı derin öğrenme algoritması https://orcid.org/0000-0002-6439-8826 Çifçi Mehmet Akif Tu Wien https://orcid.org/0000-0002-0702-2179 Canatalay Peren Jerfi İSTİNYE ÜNİVERSİTESİ https://orcid.org/0000-0001-9267-7470 Arslan Emrah İSTANBUL AYDIN ÜNİVERSİTESİ https://orcid.org/0000-0003-1330-7266 Kausar Samina University of Kotli Azad Jammu and Kashmir Kotli 02 03 2025 40 2 979 994 01 22 2024 08 04 2024 Copyright © 1986, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1986 Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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.

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