Journal ArticleDOI
An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models
Qun Niu,Hongyun Zhang,Kang Li +2 more
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TLDR
An improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for PEM fuel cell as well as solar cell models by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution.About:
This article is published in International Journal of Hydrogen Energy.The article was published on 2014-03-06. It has received 207 citations till now. The article focuses on the topics: Proton exchange membrane fuel cell & Population.read more
Citations
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Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm
TL;DR: The Chaotic Whale Optimization Algorithm (CWOA) is proposed, using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm for the parameters estimation of solar cells.
Journal ArticleDOI
Parameter estimation of solar photovoltaic (PV) cells: A review
TL;DR: In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed based on the conducted review, some recommendations for future research are provided.
Journal ArticleDOI
Parameter identification of solar cells using artificial bee colony optimization
TL;DR: The ABC (artificial bee colony) algorithm is proposed, an evolutionary method inspired by the intelligent foraging behavior of honey bees, which exhibits a better search capacity to face multi-modal objective functions in comparison with other evolutionary algorithms.
Journal ArticleDOI
Parameters identification of photovoltaic models using an improved JAYA optimization algorithm
TL;DR: An improved JAYA (IJAYA) optimization algorithm is proposed in the paper, which can obtain a highly competitive performance compared with other state-of-the-state algorithms, especially in terms of accuracy and reliability.
Journal ArticleDOI
Parameters identification of solar cell models using generalized oppositional teaching learning based optimization
TL;DR: The performance of GOTLBO is comprehensively evaluated in thirteen benchmark functions and two parameter identification problems of solar cell models, i.e., single diode model and double diode models.
References
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Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Journal ArticleDOI
Simple mathematical models with very complicated dynamics
Robert M. May,Robert M. May +1 more
TL;DR: This is an interpretive review of first-order difference equations, which can exhibit a surprising array of dynamical behaviour, from stable points, to a bifurcating hierarchy of stable cycles, to apparently random fluctuations.
Journal ArticleDOI
Evolutionary programming made faster
Xin Yao,Yong Liu,Guangming Lin +2 more
TL;DR: A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
Journal ArticleDOI
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems
TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.