Optimizing artificial neural networks using levy-chaotic mapping on wolf pack optimization algorithm for detect driving sleepiness

Abstract EN

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains.

In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized.

Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems.

However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity.

This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network.

It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm.

Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance.

The results show that the proposed algorithm achieves an accuracy of 99.3%