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Xiaohui Yuan
Researcher at Huazhong University of Science and Technology
Publications - 75
Citations - 3845
Xiaohui Yuan is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Particle swarm optimization & Population. The author has an hindex of 32, co-authored 72 publications receiving 3039 citations. Previous affiliations of Xiaohui Yuan include China Three Gorges University & Wuhan University of Technology.
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Short-term wind power prediction based on LSSVM–GSA model
TL;DR: Compared with the Back Propagation neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction.
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Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II
TL;DR: The chaotic NSGAII algorithm is used as the optimizer to search true Pareto-front of the FOPID controller and designers can implement each of them based on objective functions priority, validate the superiority of the fractional order controllers over the integer controllers.
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An improved PSO for dynamic load dispatch of generators with valve-point effects
TL;DR: In this article, an improved particle swarm optimization (IPSO) method was proposed to solve the dynamic load economic dispatch problem (DLED) with valve-point effects, where feasibility-based rules and heuristic strategies with priority list based on probability are devised to handle constraints effectively.
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An improved binary particle swarm optimization for unit commitment problem
TL;DR: Numerical results demonstrate that the IBPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.
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Monthly runoff forecasting based on LSTM–ALO model
TL;DR: In this article, the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM-ALO) in prediction of monthly runoff was investigated.