A landscape-aware particle swarm optimization for parameter identification of photovoltaic models

[1]  C. Yue,et al.  A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems , 2023, IEICE Transactions on Information and Systems.

[2]  Francis H. Shajin,et al.  A novel intelligent technique for energy management in smart home using internet of things , 2022, Appl. Soft Comput..

[3]  Hui Liu,et al.  A survey of fitness landscape analysis for optimization , 2022, Neurocomputing.

[4]  Jing J. Liang,et al.  An Evolutionary Multitasking Optimization Framework for Constrained Multiobjective Optimization Problems , 2022, IEEE Transactions on Evolutionary Computation.

[5]  Karam M. Sallam,et al.  Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer , 2022, Energy Conversion and Management.

[6]  Y. Huang,et al.  Adaptive complex network topology with fitness distance correlation framework for particle swarm optimization , 2021, Int. J. Intell. Syst..

[7]  D. Molina,et al.  A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems , 2021, Appl. Soft Comput..

[8]  K. Jana,et al.  A multiagent system based cuckoo search optimization for parameter identification of photovoltaic cell using Lambert W-function , 2022, Appl. Soft Comput..

[9]  Dorin Moldovan,et al.  Energy consumption prediction of appliances using machine learning and multi-objective binary grey wolf optimization for feature selection , 2021, Appl. Soft Comput..

[10]  Jeng-Shyang Pan,et al.  A two-phase quasi-affine transformation evolution with feedback for parameter identification of photovoltaic models , 2021, Applied Soft Computing.

[11]  Mojtaba Beiraghi,et al.  Optimal allocation of photovoltaic/wind energy system in distribution network using meta-heuristic algorithm , 2021, Appl. Soft Comput..

[12]  Wenyin Gong,et al.  A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models , 2021 .

[13]  Marcin Woźniak,et al.  Red fox optimization algorithm , 2021, Expert Syst. Appl..

[14]  Wenyin Gong,et al.  Opposition-based JAYA with population reduction for parameter estimation of photovoltaic solar cells and modules , 2021, Appl. Soft Comput..

[15]  Katherine Mary Malan,et al.  A Survey of Advances in Landscape Analysis for Optimisation , 2021, Algorithms.

[16]  Wenyin Gong,et al.  Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models , 2021 .

[17]  Xuesong Yan,et al.  A hybrid adaptive teaching–learning-based optimization and differential evolution for parameter identification of photovoltaic models , 2020 .

[18]  Fei Yu,et al.  Triple Archives Particle Swarm Optimization , 2020, IEEE Transactions on Cybernetics.

[19]  Dongxiao Niu,et al.  Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study , 2020, Appl. Soft Comput..

[20]  Dirk Sudholt,et al.  Causes and effects of fitness landscapes in unit test generation , 2020, GECCO.

[21]  Carola Doerr,et al.  Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants , 2020, GECCO.

[22]  Jing J. Liang,et al.  Evolutionary multi-task optimization for parameters extraction of photovoltaic models , 2020 .

[23]  Gabriela Ochoa,et al.  Inferring Future Landscapes: Sampling the Local Optima Level , 2020, Evolutionary Computation.

[24]  Jing J. Liang,et al.  Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models , 2020 .

[25]  Hany M. Hasanien,et al.  Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules , 2019, Energy.

[26]  Xiaofei Wang,et al.  A covariance matrix adaptation evolution strategy variant and its engineering application , 2019, Appl. Soft Comput..

[27]  Sirapat Chiewchanwattana,et al.  An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models , 2019, Renewable Energy.

[28]  Jonathan E. Rowe,et al.  Landscape Analysis of a Class of NP-Hard Binary Packing Problems , 2019, Evolutionary Computation.

[29]  Xu Chen,et al.  A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module , 2019, Applied Energy.

[30]  Ali Wagdy Mohamed,et al.  Adaptive guided differential evolution algorithm with novel mutation for numerical optimization , 2017, International Journal of Machine Learning and Cybernetics.

[31]  R. Saidur,et al.  A review on recent development for the design and packaging of hybrid photovoltaic/thermal (PV/T) solar systems , 2018, Renewable and Sustainable Energy Reviews.

[32]  Anis Sakly,et al.  Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction , 2018, Energy Conversion and Management.

[33]  Jing Liang,et al.  Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models , 2018, Applied Energy.

[34]  A. R. Jordehi.  Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules , 2018 .

[35]  Zhongqiang Wu,et al.  Application of improved bat algorithm for solar PV maximum power point tracking under partially shaded condition , 2018, Appl. Soft Comput..

[36]  Huaglory Tianfield,et al.  Biogeography-based learning particle swarm optimization , 2016, Soft Computing.

[37]  Xin Wang,et al.  Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization , 2017 .

[38]  Gabriela Ochoa,et al.  The effect of landscape funnels in QAPLIB instances , 2017, GECCO.

[39]  Renquan Lu,et al.  Learning backtracking search optimisation algorithm and its application , 2017, Inf. Sci..

[40]  Adam Prügel-Bennett,et al.  An Analysis of the Fitness Landscape of Travelling Salesman Problem , 2016, Evolutionary Computation.

[41]  Wenxiang Zhao,et al.  Parameters identification of solar cell models using generalized oppositional teaching learning based optimization , 2016 .

[42]  Kaizhu Huang,et al.  Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model , 2016, Appl. Soft Comput..

[43]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[44]  Mario A. Muñoz,et al.  Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges , 2015, Inf. Sci..

[45]  Saman K. Halgamuge,et al.  Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content , 2015, IEEE Transactions on Evolutionary Computation.

[46]  Andries Petrus Engelbrecht,et al.  Characterising the searchability of continuous optimisation problems for PSO , 2014, Swarm Intelligence.

[47]  Mohammad Ali Abido,et al.  Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms , 2013, Appl. Soft Comput..

[48]  Anula Khare,et al.  A review of particle swarm optimization and its applications in Solar Photovoltaic system , 2013, Appl. Soft Comput..

[49]  Wei Chu,et al.  Handling boundary constraints for particle swarm optimization in high-dimensional search space , 2011, Inf. Sci..

[50]  Markus Neuhäuser,et al.  Wilcoxon Signed Rank Test , 2006 .

[51]  Meiying Ye,et al.  Parameter extraction of solar cells using particle swarm optimization , 2009 .

[52]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[53]  L. Darrell Whitley,et al.  The dispersion metric and the CMA evolution strategy , 2006, GECCO.

[54]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..