Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm

被引：0
作者：
Ahmet Cevahir Cinar
机构：
[1] Selcuk University,Department of Computer Engineering, Faculty of Technology
来源：
Arabian Journal for Science and Engineering | 2020年 / 45卷
关键词：
Tree-seed algorithm; Multi-layer perceptron; Training neural network; Artificial neural network; Neural networks; Nature inspired algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The artificial neural network (ANN) is the most popular research area in neural computing. A multi-layer perceptron (MLP) is an ANN that has hidden layers. Feed-forward (FF) ANN is used for classification and regression commonly. Training of FF MLP ANN is performed by backpropagation (BP) algorithm generally. The main disadvantage of BP is trapping into local minima. Nature-inspired optimizers have some mechanisms escaping from the local minima. Tree-seed algorithm (TSA) is an effective population-based swarm intelligence algorithm. TSA mimics the relationship between trees and their seeds. The exploration and exploitation are controlled by search tendency which is a peculiar parameter of TSA. In this work, we train FF MLP ANN for the first time. TSA is compared with particle swarm optimization, gray wolf optimizer, genetic algorithm, ant colony optimization, evolution strategy, population-based incremental learning, artificial bee colony, and biogeography-based optimization. The experimental results show that TSA is the best in terms of mean classification rates and outperformed the opponents on 18 problems.
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页码：10915 / 10938
页数：23
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