Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
It is hard to predict wind power with high-precision due to its non-stationary and stochastic
nature. The wind power has developed rapidly around the world as a promising renewable …
nature. The wind power has developed rapidly around the world as a promising renewable …
Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm
Y Zhu, N Yousefi - International journal of hydrogen energy, 2021 - Elsevier
In this paper, a new optimization algorithm called Adaptive Sparrow Search Algorithm
(ASSA) is proposed for optimal model parameters identification of the proton exchange …
(ASSA) is proposed for optimal model parameters identification of the proton exchange …
A fuzzy C-means algorithm for optimizing data clustering
Big data has increasingly become predominant in many research fields affecting human
knowledge, including medicine and engineering. Cluster analysis, or clustering, is widely …
knowledge, including medicine and engineering. Cluster analysis, or clustering, is widely …
Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization
M Pan, C Li, R Gao, Y Huang, H You, T Gu… - Journal of Cleaner …, 2020 - Elsevier
Accurate prediction of photovoltaic (PV) power for an ultra-short term can improve the usage
of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term …
of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term …
Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm
intended to improve the optimization capabilities of the conventional grey wolf optimizer in …
intended to improve the optimization capabilities of the conventional grey wolf optimizer in …
Accelerated two-stage particle swarm optimization for clustering not-well-separated data
Cluster analysis is a data mining technique that has been widely used to exploit useful
information in a great amount of data. Because of their evaluation mechanism based on an …
information in a great amount of data. Because of their evaluation mechanism based on an …
Variance-based differential evolution algorithm with an optional crossover for data clustering
The differential evolution optimization-based clustering techniques are powerful, robust and
more sophisticated than the conventional clustering methods due to their stochastic and …
more sophisticated than the conventional clustering methods due to their stochastic and …
Forecasting hydropower generation by GFDL‐CM3 climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search …
H Wang, X Wu, F Gholinia - Concurrency and Computation …, 2021 - Wiley Online Library
One of the most sensitive factors affecting hydropower generation is climate change. The
objective of this study is to forecast the hydropower generation under the influence of climate …
objective of this study is to forecast the hydropower generation under the influence of climate …
Information-utilization strengthened equilibrium optimizer
X Zhang, Q Lin - Artificial Intelligence Review, 2022 - Springer
Equilibrium Optimizer (EO) is a novel meta-heuristic algorithm proposed in 2020 and it has a
unique search mechanism and good optimization performance. To further improve its …
unique search mechanism and good optimization performance. To further improve its …
An improved multi-population ensemble differential evolution
L Tong, M Dong, C **g - Neurocomputing, 2018 - Elsevier
Differential evolution (DE) is a population-based stochastic optimization technique that can
be applied to solve global optimization problems. The selected mutation strategies and the …
be applied to solve global optimization problems. The selected mutation strategies and the …