Journal ArticleDOI
Policy Iteration Reinforcement Learning-based control using a Grey Wolf Optimizer algorithm
TLDR
In this article , a new reinforcement learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs) is presented.About:
This article is published in Information Sciences.The article was published on 2022-03-01. It has received 84 citations till now. The article focuses on the topics: Reinforcement learning & Computer science.read more
Citations
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Journal ArticleDOI
Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems
TL;DR: Zhang et al. as mentioned in this paper proposed a new bio-inspired meta-heuristic algorithm, named artificial rabbits optimization (ARO), which is applied to the fault diagnosis of a rolling bearing, in which the back-propagation (BP) network optimized by ARO is developed.
Journal ArticleDOI
Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy
TL;DR: An improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems and can provide a more reliable solution than other well-known algorithms.
Journal ArticleDOI
A fuzzy adaptive zeroing neural network with superior finite-time convergence for solving time-variant linear matrix equations
TL;DR: In this article , a fuzzy adaptive zeroing neural network (FAZNN) model was proposed to solve the time-variant linear matrix equation (TVLME) problem.
Journal ArticleDOI
A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals
Yufei Sha,J. K. Faber,Shuiping Gou,Bo Liu,Wei Liu,Stefan Schramm,Horst Stoecker,T. Steckenreiter,Domagoj Vnucec,Nadine Wetzstein,A. Widl,Kai Zhou +11 more
TL;DR: In this paper , a multi-task learning framework for simultaneous cavitation detection and cavitation intensity recognition was proposed using 1-D double hierarchical residual networks (1-D DHRN) for analyzing valve acoustic signals.
Journal ArticleDOI
Using quantum amplitude amplification in genetic algorithms
TL;DR: In this paper , a quantum genetic sampling (QGS) algorithm is proposed to replace selection operators so as to provide an increased population diversity in genetic evolution and reduce the possibility that the optimization process converge to low quality solutions.
References
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
Deep Reinforcement Learning: A Brief Survey
TL;DR: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world as discussed by the authors.
Journal ArticleDOI
Slime mould algorithm: A new method for stochastic optimization
Shimin Li,Huiling Chen,Mingjing Wang,Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili +5 more
TL;DR: The proposed slime mould algorithm has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity.
Journal ArticleDOI
Multi-objective grey wolf optimizer
TL;DR: A novel multi-objective algorithm called Multi-Objective Grey Wolf Optimizer (MOGWO) is proposed in order to optimize problems with multiple objectives for the first time.
Journal ArticleDOI
Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
TL;DR: In this article, the authors describe the use of reinforcement learning to design feedback controllers for discrete and continuous-time dynamical systems that combine features of adaptive control and optimal control, which are not usually designed to be optimal in the sense of minimizing user-prescribed performance functions.
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