Differential Evolution: A review of more than two decades of research
Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …
frequently used algorithms for solving complex optimization problems. Its flexibility and …
A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries
Lithium-ion batteries have been generally used in industrial applications. In order to ensure
the safety of the power system and reduce the operation cost, it is particularly important to …
the safety of the power system and reduce the operation cost, it is particularly important to …
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease
cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR) …
cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR) …
An improved differential evolution algorithm and its application in optimization problem
W Deng, S Shang, X Cai, H Zhao, Y Song, J Xu - Soft Computing, 2021 - Springer
The selection of the mutation strategy for differential evolution (DE) algorithm plays an
important role in the optimization performance, such as exploration ability, convergence …
important role in the optimization performance, such as exploration ability, convergence …
Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization
W Deng, S Shang, X Cai, H Zhao, Y Zhou… - Knowledge-Based …, 2021 - Elsevier
In order to overcome the low solution efficiency, insufficient diversity in the later search
stage, slow convergence speed and a high search stagnation possibility of differential …
stage, slow convergence speed and a high search stagnation possibility of differential …
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 …
A survey on deep learning for software engineering
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …
and an improved model training method to break the bottleneck of neural network …
Evolutionary algorithms and neural networks
S Mirjalili - Studies in computational intelligence, 2019 - Springer
This book focuses on both theory and application of evolutionary algorithms and artificial
neural networks. An attempt is made to make a bridge between these two fields with an …
neural networks. An attempt is made to make a bridge between these two fields with an …
A review on prognostics and health management (PHM) methods of lithium-ion batteries
Batteries are prevalent energy providers for modern systems. They can also be regarded as
storage units for renewable and sustainable energy. Failures of batteries can bring huge …
storage units for renewable and sustainable energy. Failures of batteries can bring huge …
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 …