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

Capacity optimization of battery and thermal energy storage systems considering system energy efficiency and user comfort DOI
Yuanyuan Chen, Shaobing Yang,

Yibo Wang

et al.

Electric Power Systems Research, Journal Year: 2025, Volume and Issue: 243, P. 111480 - 111480

Published: Feb. 8, 2025

Language: Английский

Citations

0
Sturnus vulgaris escape algorithm and its application to mechanical design DOI Creative Commons

Y. G. Liu,

Yaping Fan,

Jiaxing Ma

et al.

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

Published: Feb. 24, 2025

Practical engineering optimization problems are characterized by high dimensionality, non-convexity, and non-linearity, the use of optimizers to provide better quality solutions target problem in an acceptable time is a hot research topic field optimal design. In this paper, inspired Sturnus vulgaris escape behavior, Vulgaris Escape Algorithm (SVEA) proposed high-performance optimizer for complex problems. The algorithm composed exploration exploitation strategies, controlled fixed parameters. strategies include High-Altitude Strategy Wave 1, while consist Cordon Line 2. enhances capabilities reorganizing subgroups, preventing leader individuals from overlapping, avoiding collisions between individuals. conducts refined searches around high-value regions, further improving precision. Strategies 1 2 help population local optima prevent over-spreading. performance SVEA evaluated through employment 23 benchmark test functions CEC2017 set, with subsequent comparison undertaken nine statE − of-thE art meta-heuristic algorithms. outcomes evaluation demonstrate that attains top ranking identified as best-performing across all sets. A statistical analysis reveals solution set exhibits superior other algorithms, discrepancy being deemed be statistically significant. Finally, applied five real-world problems, providing satisfying constraints.

Language: Английский

Citations

0
Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement DOI
Jagrati Talreja,

Divya Chauhan

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 197 - 228

Published: Jan. 1, 2025

Language: Английский

Citations

0
Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine DOI Open Access

Farhat Nasim,

Shahida Khatoon,

Ibraheem

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2454 - 2454

Published: March 11, 2025

Wind energy is essential for promoting sustainability and renewable power solutions. However, ensuring stability consistent performance in DFIG-based wind turbine systems (WTSs) remains challenging due to rapid speed variations, grid disturbances, parameter uncertainties. These fluctuations result instability, increased overshoot, prolonged settling times, negatively impacting compliance system efficiency. Conventional proportional-integral (PI) controllers are simple effective steady-state conditions, but they lack adaptability dynamic situations. Similarly, artificial intelligence (AI)-based controllers, such as fuzzy logic (FLCs) neural networks (ANNs), improve suffer from high computational demands training complexity. To address these limitations, this paper presents a hybrid adaptive neuro-fuzzy inference (ANFIS)-PI controller WTS. The proposed integrates with network-based learning, allowing real-time optimization of control parameters. Implemented within the rotor-side converter (RSC) grid-side (GSC), ANFIS enhances reactive management, compliance, overall stability. was tested under step signal varying 10 m/s 12 evaluate its robustness. simulation results confirmed that ANFIS-PI significantly improved compared conventional PI controller. Specifically, it reduced rotor overshoot by 3%, torque 12.5%, active 2%, DC link voltage 20%. Additionally, shortened time 50% speed, 25% torque, 33% power, 16.7% voltage, faster stabilization, enhanced response, greater improvements establish an advanced, computationally efficient, scalable solution enhancing reliability WTS, facilitating seamless integration into modern grids.

Language: Английский

Citations

0
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

Citations

0