Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms

The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training meth...

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Bibliographic Details
Published in:International journal of information retrieval research 2022-01, Vol.12 (1), p.1-21
Main Authors: Bousmaha, Rabab, Hamou, Reda Mohamed, Amine, Abdelmalek
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Mathematical optimization
Methods
Neural networks
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Summary:The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.
ISSN:2155-6377
2155-6385
DOI:10.4018/IJIRR.289569