Gradient-based optimizer (gbo): a review, theory, variants, and applications
This paper introduces a comprehensive survey of a new population-based algorithm so-
called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as …
called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as …
Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems
S Ravichandran, P Manoharan, P Jangir… - Scientific Reports, 2023 - nature.com
The primary objective of this study is to delve into the application and validation of the
Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired …
Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired …
A reliable optimization framework for parameter identification of single‐diode solar photovoltaic model using weighted velocity‐guided grey wolf optimization algorithm …
In estimating the parameters of the five unknown parameters Single‐Diode Model (SDM) of
the solar photovoltaic (PV) model, a non‐linear equation for the PV cell current is typically …
the solar photovoltaic (PV) model, a non‐linear equation for the PV cell current is typically …
[HTML][HTML] An enhanced Gradient-based Optimizer for parameter estimation of various solar photovoltaic models
The performance of a PhotoVoltaic (PV) system could be inferred from the features of its
current–voltage relationships, but the PV model parameters are uncertain. Because of its …
current–voltage relationships, but the PV model parameters are uncertain. Because of its …
A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic …
Photovoltaic (PV) modeling is becoming an increasingly popular field of study. When it
comes to the effective performance and reliability of PV systems, the reliability of PV models …
comes to the effective performance and reliability of PV systems, the reliability of PV models …
Running city game optimizer: A game-based metaheuristic optimization algorithm for global optimization
B Ma, Y Hu, P Lu, Y Liu - Journal of Computational Design and …, 2023 - academic.oup.com
As science and technology improve, more and more complex global optimization difficulties
arise in real-life situations. Finding the most perfect approximation and optimal solution …
arise in real-life situations. Finding the most perfect approximation and optimal solution …
[HTML][HTML] Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques …
M Abdel-Basset, R Mohamed, IM Hezam… - Expert Systems with …, 2024 - Elsevier
Accurately estimating the unknown parameters of the photovoltaic (PV) models based on the
measured voltage-current data is a challenging optimization problem due to its high …
measured voltage-current data is a challenging optimization problem due to its high …
Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm
N Kullampalayam Murugaiyan, K Chandrasekaran… - Scientific Reports, 2024 - nature.com
Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter
extraction presents a challenging problem. This challenge is exacerbated by the propensity …
extraction presents a challenging problem. This challenge is exacerbated by the propensity …
Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer
The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their
parameters. Among the numerous optimization techniques, the effectiveness of each often …
parameters. Among the numerous optimization techniques, the effectiveness of each often …
IGJO: an improved golden jackel optimization algorithm using local escaping operator for feature selection problems
Feature Selection (FS) is an essential process that is implicated in data mining and machine
learning for data preparation by removing redundant and irrelevant features, thereby falling …
learning for data preparation by removing redundant and irrelevant features, thereby falling …