[HTML][HTML] Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review
M Kaveh, MS Mesgari - Neural Processing Letters, 2023 - Springer
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 …
and deep learning (DL) architectures is considered one of the most challenging machine …
[HTML][HTML] Elephant herding optimization: variants, hybrids, and applications
Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization
algorithm based on the herding behavior of elephants. EHO uses a clan operator to update …
algorithm based on the herding behavior of elephants. EHO uses a clan operator to update …
Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite
The growing concern about global climate change and its adverse impacts on societies is
putting severe pressure on the construction industry as one of the largest producers of …
putting severe pressure on the construction industry as one of the largest producers of …
Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction
An artificial neural network (ANN) that mimics the information processing mechanisms and
procedures of neurons in human brains has achieved a great success in many fields, eg …
procedures of neurons in human brains has achieved a great success in many fields, eg …
Optimizing connection weights in neural networks using the whale optimization algorithm
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 …
challenges in machine learning and has attracted many researchers recently. The main …
An intensify Harris Hawks optimizer for numerical and engineering optimization problems
Abstract Recently developed Harris Hawks Optimization has virtuous behavior for finding
optimum solution in search space. However, it easily get trapped into local search space for …
optimum solution in search space. However, it easily get trapped into local search space for …
Elephant herding optimization
In this paper, a new kind of swarm-based metaheuristic search method, called Elephant
Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is …
Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is …
Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems
Earthworms can aerate the soil with their burrowing action and enrich the soil with their
waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired …
waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired …
Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm
W Qiao, M Khishe, S Ravakhah - Ocean Engineering, 2021 - Elsevier
Considering heterogeneities and difficulties in the classification of underwater passive
targets, this paper proposes the use of Local Wavelet Acoustic Pattern (LWAP) and Multi …
targets, this paper proposes the use of Local Wavelet Acoustic Pattern (LWAP) and Multi …
An efficient hybrid multilayer perceptron neural network with grasshopper optimization
This paper proposes a new hybrid stochastic training algorithm using the recently proposed
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …