Improved Artificial Neural Networks Based Whale Optimization Algorithm

被引：0

作者：

Ibrahem Alhayali R.A. ^{[1
]}

Aladamey M.K.A.A. ^{[2
]}

Subhi M.R. ^{[3
]}

Mohammed M.A. ^{[2
]}

Amir A. ^{[4
]}

Abdalkareem Z.A. ^{[2
]}

机构：

[1] Department of Architecture Engineering, College of Engineering, University of Diyala, Diyala

[2] Alimam Aladham University College, Baghdad

[3] Department of Petroleum System Control Engineering, College of Petroleum Processes Engineering, Tikrit University

[4] Faculty of Engineering & Electronic Engineering Technology, University Malaysia

来源：

Iraqi Journal for Computer Science and Mathematics | 2023年 / 4卷 / 03期

关键词：

artificial neural network; backpropagation; metaheuristics; whale optimization algorithm;

D O I：

10.52866/ijcsm.2023.02.03.015

中图分类号：

学科分类号：

摘要：

Owing to the increasing interest in artificial neural networks (ANNs) across various fields of study, many studies have focused on enhancing their performance through the utilisation of different learning algorithms. This study examines the use of the Whale Optimization Algorithm (WOA) as a training algorithm to improve the classification accuracy of ANNs. To achieve a high level of classification accuracy with ANN models, it is imperative to ensure that the model is appropriately designed in terms of the employed structure, training algorithm and activation function. In this work, WOA was adopted to train ANN models using 10 well-known datasets sourced from the UCI machine learning repository. The classification accuracy of a WOA-trained ANN was compared with that of a backpropagation-trained ANN, and the results showed that the WOA-trained ANN exhibited superior performance. © 2023 College of Education, Al-Iraqia University. All rights reserved

引用

页码：195 / 202

页数：7

共 25 条

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