Geliştirilmiş Atom Arama Optimizasyon Algoritması ile Çok Katmanlı Algılayıcı Eğitimi

Year 2021, Volume: 11 Issue: 21, 71 - 79, 30.06.2021

Davut İzci

Abstract

Bu makalede atom arama optimizasyonu (ASO) ve benzetilmiş tavlama (SA) algoritmalarının hibritleştirilmesiyle geliştirilen ve iASO olarak isimlendirilen yeni bir hibrit algoritma ele alınmaktadır. SA tekniğinin kullanımı ile ASO algoritmasının arama yeteneği güçlendirilmiştir. Önerilen hibrit algoritmanın doğrusal olmayan sistemleri optimize etmedeki yeteneğini gözlemlemek üzere çok katmanlı algılayıcıyı (MLP) eğiticisi olarak kullanılmıştır. Iris, Balloon, XOR, Breast Cancer ve Heart olmak üzere çeşitli veri kümeleri kullanılmış ve elde edilen sonuçlar orijinal ASO, sinüs kosinüs algoritması (SCA), parçacık sürüsü optimizasyonu (PSO), karınca kolonisi optimizasyonu (ACO) ve gri kurt optimizasyonu (GWO) gibi rekabetçi algoritmalar kullanılarak oluşturulmuş diğer MLP eğiticileri ile karşılaştırılmıştır. Sonuçlar, önerilen yaklaşımla daha düşük ortalama kare hatasının (MSE) ortalama ve standart sapmasının elde edildiğini göstermiş ve dolayısıyla daha iyi performansının olduğunu açıkça göstermiştir.

Keywords

Çok katmanlı algılayıcı, sezgisel-üstü algoritmalar, atom arama optimizasyonu, benzetilmiş tavlama algoritması

A Novel Improved Atom Search Optimization Algorithm for Training Multilayer Perceptron

Abstract

A novel hybrid algorithm developed by merging atom search optimization (ASO) and simulated annealing (SA) algorithms is presented. The search capability of ASO was improved by using simulated annealing (SA) algorithm. The proposed hybrid algorithm was named as iASO and and used for training multilayer perceptron (MLP) to observe its ability for optimizing non-linear systems. Several datasets (Iris, Balloon, XOR, Breast cancer and Heart) were used, and the obtained results were compared with respective recent competitive algorithms such as original ASO, sine cosine algorithm (SCA), particle swarm optimization (PSO), ant colony optimization (ACO) and grey wolf optimization (GWO). The results clearly indicated the performance of the proposed algorithm to be better as the lower average and standard deviation of mean square error were achieved via the proposed approach.

Keywords

Multilayer perceptron, metaheuristic algorithms, atom search optimization, simulated annealing

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