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Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms

Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms

Rabab Bousmaha, Reda Mohamed Hamou, Abdelmalek Amine
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 21
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.289569
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MLA

Bousmaha, Rabab, et al. "Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms." IJIRR vol.12, no.1 2022: pp.1-21. http://doi.org/10.4018/IJIRR.289569

APA

Bousmaha, R., Hamou, R. M., & Amine, A. (2022). Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-21. http://doi.org/10.4018/IJIRR.289569

Chicago

Bousmaha, Rabab, Reda Mohamed Hamou, and Abdelmalek Amine. "Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-21. http://doi.org/10.4018/IJIRR.289569

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Abstract

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.