Search Results - (( wolf optimization method algorithm ) OR ( global optimization method algorithm ))
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Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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A conjugate gradient method with Strong Wolfe-Powell line search for unconstrained optimization
Published 2016“…Computational results for a set consisting of 138 unconstrained optimization test problems showed that this new conjugate gradient algorithm seems to converge more stable and is superior to other similar methods in many situations.…”
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Extension of RMIL conjugate gradient method for unconstrained optimization / Nur Idalisa Norddin
Published 2023“…The Conjugate Gradient (CG) methods have significantly contributed to solving Unconstrained Optimization (UO) problems. …”
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A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
Published 2024“…Extensive studies prove the proposed algorithm outperforms bat algorithm (BA), improved grey wolf optimizer (IGWO), conventional PSO and P&O, with convergence time shorter than 0.3 s and tracking accuracy above 99% under different complex PSCs. � 2010-2012 IEEE.…”
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Enhancing performance of global path planning for mobile robot through Alpha–Beta Guided Particle Swarm Optimization (ABGPSO) algorithm
Published 2025“…Through extensive simulations across various static environment maps, we demonstrate that the ABGPSO algorithm outperforms existing state-of-the-art optimization techniques, including Genetic Algorithms (GA), Grey Wolf Optimization (GWO), and modern optimizers like the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO) and Reptile search algorithm (RSA). …”
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Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions
Published 2022“…However, the arduous task in the electrical industry is to contribute to the uninterrupted power supply by the PV system as a result of partial shading conditions (PSC). To track the global maximum peak power (GMPP) instead of local maxima peak power (LMPP), the combination of gray wolf optimization (GWO) and differential evolution (DE) algorithm is hybridized (GWO-DE) in this work. …”
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An improvement of BFGS by applying n-th section method for solving unconstrained optimization / Nurul Atikah Mohamed Ramli
Published 2019“…Optimization is one of mathematics field that greatly developed when Quasi-newton method was presented to solve the unconstrained optimization problem. …”
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A conjugate gradient method with descent properties under strong Wolfe line search
Published 2017“…The conjugate gradient (CG) method is one of the optimization methods that are often used in practical applications. …”
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A new hybrid three-term LS-CD conjugate gradient in solving unconstrained optimization problems
Published 2024“…The Conjugate Gradient (CG) method is renowned for its rapid convergence in optimization applications. …”
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4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer
Published 2023“…However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. …”
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Dynamic Economic Dispatch For Power System
Published 2016“…Through an appropriate utilization of the structural features of the model, a solution algorithm based on Particle Swarm Optimization is developed. …”
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Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer
Published 2024“…This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. …”
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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection
Published 2023“…While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. …”
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A novel MPPT approach for photovoltaic system using Pelican optimization and high-gain DC–DC converter
Published 2025“…In response to these needs, this study introduces the Pelican Optimization Algorithm (POA), a novel nature-inspired stochastic optimization technique designed to track the Maximum Power Point (MPP) of solar sources with high precision. …”
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…—Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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Urban connected vehicle lane planning based on improved Frank Wolfe algorithm
Published 2025“…The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”
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