Scopus
YÖKSİS DOI Eşleşti
SJR Q2
Vortex search optimization algorithm for training of feed-forward neural network
International Journal of Machine Learning and Cybernetics · Mayıs 2021
YÖKSİS Kayıtları
Vortex search optimization algorithm for training of feed-forward neural network
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS · 2021 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Integration search strategies in tree seed algorithm for high dimensional function optimization
2020 ISSN: 1868-8071 SCI Q2
Doç. Dr. AHMET CEVAHİR ÇINAR →
Vortex search optimization algorithm for training of feed-forward neural network
2021 ISSN: 1868-8071 SCI-Expanded Q2
Doç. Dr. TAHİR SAĞ →
Makale Bilgileri
ISSN18688071
Yayın TarihiMayıs 2021
Cilt / Sayfa12 · 1517-1544
Scopus ID2-s2.0-85100454016
Özet
Training of feed-forward neural-networks (FNN) is a challenging nonlinear task in supervised learning systems. Further, derivative learning-based methods are frequently inadequate for the training phase and cause a high computational complexity due to the numerous weight values that need to be tuned. In this study, training of neural-networks is considered as an optimization process and the best values of weights and biases in the structure of FNN are determined by Vortex Search (VS) algorithm. The VS algorithm is a novel metaheuristic optimization method recently developed, inspired by the vortex shape of stirred liquids. VS fulfills the training task to set the optimal weights and biases stated in a matrix. In this context, the proposed VS-based learning method for FNNs (VS-FNN) is conducted to analyze the effectiveness of the VS algorithm in FNN training for the first time in the literature. The proposed method is applied to six datasets whose names are 3-bit XOR, Iris Classification, Wine-Recognition, Wisconsin-Breast-Cancer, Pima-Indians-Diabetes, and Thyroid-Disease. The performance of the proposed algorithm is analyzed by comparing with other training methods based on Artificial Bee Colony Optimization (ABC), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Genetic Algorithm (GA) and Stochastic Gradient Descent (SGD) algorithms. The experimental results show that VS-FNN is generally leading and competitive. It is also said that VS-FNN can be used as a capable tool for neural networks.
Yazarlar (2)
1
Tahir Saǧ
2
Zainab Abdullah Jalil Jalil
Anahtar Kelimeler
Classification
FNN
Optimization
Training neural-networks
Vortex search
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
International Journal of Machine Learning and Cybernetics
Q2
SJR Skoru0,699
H-Index79
YayıncıSpringer Science + Business Media
ÜlkeUnited States
Artificial Intelligence (Q2)
Computer Vision and Pattern Recognition (Q2)
Software (Q2)
Metrikler
24
Atıf
2
Yazar
5
Anahtar Kelime