[HTML][HTML] Composite adaptation and learning for robot control: A survey

K Guo, Y Pan - Annual Reviews in Control, 2023 - Elsevier
Composite adaptation and learning techniques were initially proposed for improving
parameter convergence in adaptive control and have generated considerable research …

Recent advances in Grey Wolf Optimizer, its versions and applications

SN Makhadmeh, MA Al-Betar, IA Doush… - IEEE …, 2023 - ieeexplore.ieee.org
The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm
intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's …

Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems

L Wang, Q Cao, Z Zhang, S Mirjalili, W Zhao - Engineering Applications of …, 2022 - Elsevier
In this paper, a new bio-inspired meta-heuristic algorithm, named artificial rabbits
optimization (ARO), is proposed and tested comprehensively. The inspiration of the ARO …

Neural network-based control using actor-critic reinforcement learning and grey wolf optimizer with experimental servo system validation

IA Zamfirache, RE Precup, RC Roman… - Expert Systems with …, 2023 - Elsevier
This paper introduces a novel reference tracking control approach implemented using a
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …

SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning

G Hu, J Zhong, G Wei - Expert Systems with Applications, 2023 - Elsevier
The honey badger algorithm (HBA) is a meta-heuristic optimization algorithm that simulates
the foraging behavior of honey badgers. Since the algorithm is prone to premature …

Model-based reinforcement learning control of electrohydraulic position servo systems

Z Yao, X Liang, GP Jiang, J Yao - IEEE/ASME Transactions on …, 2022 - ieeexplore.ieee.org
Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement
learning as a feasible complex problem solver, the research on reinforcement learning …

Prediction of ammonia contaminants in the aquaculture ponds using soft computing coupled with wavelet analysis

TV Nagaraju, BM Sunil, B Chaudhary, CD Prasad… - Environmental …, 2023 - Elsevier
Intensive aquaculture practices generate highly polluted organic effluents such as biological
oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron …

State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network

Y Wei, D Wu - Expert Systems with Applications, 2024 - Elsevier
Graph convolutional networks (GCNs) have been increasingly used to predict the state of
health (SOH) and remaining useful life (RUL) of batteries. However, conventional GCNs …

Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems

C Zhong, G Li, Z Meng, W He - Expert Systems with Applications, 2023 - Elsevier
The equilibrium optimizer (EO) is a recently proposed physics-based metaheuristic
algorithm inspired by the dynamic mass balance on a control volume. However, EO may …

[HTML][HTML] Optimal scheduling of electric vehicle charging operations considering real-time traffic condition and travel distance

Y An, Y Gao, N Wu, J Zhu, H Li, J Yang - Expert Systems with Applications, 2023 - Elsevier
As the number of electric vehicles (EVs) increases rapidly, the problem of electric vehicle
charging has widely become a concern. Therefore, considering the fact that charging time …