A Novel Improved Gradient‐Based Optimizer for Single‐Sensor Global MPPT of PV System DOI Creative Commons
Hegazy Rezk, Usama Hamed Issa, Anas Bouaouda

et al.

Journal of Mathematics, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Gradient‐Based Optimizer (GBO) is a highly mathematics‐based metaheuristic algorithm that has garnered significant attention since its introduction. It offers several inherent advantages, such as low computational complexity, rapid convergence, and easy implementation. However, GBO some drawbacks, including lack of population diversity tendency to get trapped in local optima. To address these shortcomings, this research introduces an improved version (iGBO). In iGBO, introducing the Sobol sequence strategy ensures higher‐quality initial enhances convergence speed. Additionally, new modified Local Escaping Operator (LEO) proposed, which incorporates sine‐cosine operator DCS/Xbest/Current‐to‐2rand strategy. This LEO improves optimization efficiency boosts search capability, helping avoid The superiority iGBO thoroughly verified through comparisons with original well‐known newly developed algorithms on IEEE CEC’2022 benchmark suite. Furthermore, proposed approach applied extract photovoltaic system’s global maximum power point (MPP) under shading conditions. Three different patterns are considered assess reliability iGBO. performance compared leading algorithms, Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Black Widow (BWOA), Pelican OA (POA), Chimp (ChOA), Osprey (OOA), GBO. results reveal iGBO‐based MPPT consistently outperforms competitors identifying MPP various conditions followed by PSO, while RSA performs least effectively.

Language: Английский

Constrained search space selection based optimization approach for enhanced reduced order approximation of interconnected power system models DOI Creative Commons
Bala Bhaskar Duddeti, Asim Kumar Naskar, V. P. Meena

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 7, 2025

Metaheuristic search-based optimization strategies have recently emerged to obtain approximated models for interconnected complex power systems. However, these algorithms are frequently criticized randomly selecting lower and upper search space boundaries taking longer simulate. The incorrect selection of suitable each unknown decision variable may result in an inaccurate or unstable reduced model. This proposal introduces interim model (IRM) concept select a tight solution the algorithm. balanced residualization method (BRM) obtains IRM, geometric mean (GMO) algorithm tunes coefficients. proposed has appealing feature: IRM obtained by BRM structures GMO rather than leaving it completely arbitrary. finds ideal coefficients minimizing weighted error index. primary benefit employing IRM-based limitations is that they guarantee focused with viable answers stability. Furthermore, maintaining transient gain mitigates BRM's high-frequency spectrum disadvantage. Three system from literature support method, contrasting state-of-the-art MOR methodologies.

Language: Английский

Citations

2
Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm DOI
Walid Merrouche, Badis Lekouaghet, Elouahab Bouguenna

et al.

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 76, P. 109891 - 109891

Published: Nov. 29, 2023

Language: Английский

Citations

28
MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems DOI Creative Commons
Pranav Mehta, Sumit Kumar, Ghanshyam G. Tejani

et al.

Journal of Optimization, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by foraging behavior brown bears. proposed Multiobjective (MOBBO) algorithm is applied to five optimization problems, including 10‐bar, 25‐bar, 60‐bar, 72‐bar, and 942‐bar trusses, aiming minimize both mass maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO’s superior convergence, solution diversity, effectiveness addressing highly hypervolume (HV) inverted generational distance (IGD) metrics place MOBBO first rank according Friedman test, with an average standard deviation 0.0002. Moreover, spacing‐to‐extent (STE) (GD) second. final test highlights overall dominance, achieving rank. Best Pareto plots, diversity graphs, box plot analyses further suggest performance convergence compared existing algorithms. Therefore, can be effectively various tasks industry, offering refined global solutions contributing valuable insights field

Language: Английский

Citations

10
Parameter estimation in single-phase transformers via the generalized normal distribution optimizer while considering voltage and current measurements DOI Creative Commons

Juan David Camelo-Daza,

Diego Noel Betancourt-Alonso,

Oscar Danilo Montoya

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101760 - 101760

Published: Jan. 9, 2024

This research addresses, from a perspective of metaheuristic optimization, the problem regarding parametric estimation in single-phase transformers while considering voltage and current measures at terminals transformer weighing linear loads. Transformer is modeled as nonlinear order to minimize mean square error between calculated variables measurements taken. The nonlinearities are associated with Kirchhoff's first second laws applied equivalent electrical circuit transformer. optimization solved by applying algorithm known generalized normal distribution optimizer (GNDO), which uses evolution rules that allow exploring exploiting solution space via classical probability function based on distributions. Numerical results three test 20, 45, 112.5 kVA demonstrate effectiveness robustness proposed GNDO approach when compared other optimizers reported literature, such crow search algorithm, coyote exact model using fmincon solver MATLAB software. All numerical simulations confirm potential deal complex problems engineering science promising low computational effort.

Language: Английский

Citations

9
Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms DOI Creative Commons
Hossein Moayedi, Azfarizal Mukhtar, Nidhal Ben Khedher

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Feb. 29, 2024

Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses inputs GDP G8 nations (from 1990 to 2016) depending on utilization various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) combined with nature-inspired optimization algorithms, such Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Algorithm (WOA), Vortex Search algorithm (VS), Earthworm (EWA), create a dependable predictive network that takes complexity problem into account. Our key contributions lie in developing comprehensively evaluating these hybrid models assessing their efficacy capturing intricate dynamics carbon emissions. The found TLBO VS outperform other algorithms emission computation accuracy. has higher training MSE (3.6778) lower testing (4.4673), suggesting larger squared errors data MSE, less overfitting due better generalization set.

Language: Английский

Citations

9
Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio DOI Creative Commons
Terapong Boonraksa, Promphak Boonraksa, Watcharakorn Pinthurat

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 36280 - 36295

Published: Jan. 1, 2024

Across the globe, adoption of electric vehicles (EVs), particularly in mass transit systems such as buses (E-bus), is on rise modern cities. This surge attributed to their environmentally friendly nature, zero carbon emissions, and absence engine noise. However, charging E-bus batteries could impact peak demand main grid its overall serviceability, especially when numerous are charged simultaneously. scenario may also lead increased energy costs. To address previously mentioned issue, battery swapping employed at station lieu conventional charging. In this paper, approach utilized establish optimal schedule for E-buses, taking into account both costs peak-to-average ratio (PAR). The stations incorporate photovoltaic (PV) power generation source. Three metaheuristic algorithms—namely, binary bat algorithm (BBA), whale optimization (WOA), grey wolf optimizer (GWO)—are identify conditions. simulation results demonstrate that integrating with a PV system an can effectively lower PAR compared traditional methods stations. derived through GWO technique outperforms those obtained from WOA BBA techniques. resulted notable reduction 758.41 580.73 kW, corresponding 23.43% decrease demand. integration scheduling installation significant 27.63% As per results, optimized has potential enhance serviceability station.

Language: Английский

Citations

9
Using Crafted Features and Polar Bear Optimization Algorithm for Short-Term Electric Load Forecast System DOI Creative Commons

Mansi Bhatnagar,

Gregor Rozinaj, Radoslav Vargic

et al.

Energy and AI, Journal Year: 2025, Volume and Issue: unknown, P. 100470 - 100470

Published: Jan. 1, 2025

Language: Английский

Citations

1
A multi-faceted review of wind turbine optimization techniques: metaheuristics and related issues DOI Creative Commons
Hegazy Rezk, A.G. Olabi,

Tabbi Wilberforce

et al.

International Journal of Thermofluids, Journal Year: 2025, Volume and Issue: unknown, P. 101077 - 101077

Published: Jan. 1, 2025

Language: Английский

Citations

1
Chinese Pangolin Optimizer: a novel bio-inspired metaheuristic for solving optimization problems DOI
Zhiqing Guo, Guangwei Liu, Feng Jiang

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(4)

Published: Feb. 17, 2025

Language: Английский

Citations

1
A metaheuristic Multi-Objective optimization of energy and environmental performances of a Waste-to-Energy system based on waste gasification using particle swarm optimization DOI

Xiaotuo Qiao,

Jiaxin Ding,

She Chen

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 317, P. 118844 - 118844

Published: Aug. 6, 2024

Language: Английский

Citations

8