From the past few decades many nature inspired algorithms have been developed and
gaining more popularity because of their effectiveness in solving problems of distinct …

The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

The particle swarm optimization (PSO) is a population-based stochastic optimization
technique by the social behavior of bird flocking and fish schooling. The PSO has a high …

The learning process of artificial neural networks is considered as one of the most difficult
challenges in machine learning and has attracted many researchers recently. The main …

Considering heterogeneities and difficulties in the classification of underwater passive
targets, this paper proposes the use of Local Wavelet Acoustic Pattern (LWAP) and Multi …

This paper proposes a new hybrid stochastic training algorithm using the recently proposed
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …

This chapter proposes an efficient hybrid training technique (ALOMLP) based on the Ant
Lion Optimizer (ALO) to be utilized in dealing with Multi-Layer Perceptrons (MLPs) neural …

Abstract The Multi-Layer Perceptron (MLP), as one of the most-widely used Neural Networks
(NNs), has been applied to many practical problems. The MLP requires training on specific …

This paper employs the recently proposed nature-inspired algorithm called Multi-Verse
Optimizer (MVO) for training the Multi-layer Perceptron (MLP) neural network. The new …

Artificial neural network (ANN) is an information processing paradigm that loosely models
the thinking patterns of the human brain with specifications such as real-time learning, self …