An evolutionary multitasking algorithm for multi-objective feature selection using dual-perspective reduction

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 152, С. 110764 - 110764

Опубликована: Апрель 15, 2025

Язык: Английский

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Identifying the unknown parameters of PEM fuel cells based on a human-inspired optimization algorithm

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 129, С. 222 - 235

Опубликована: Апрель 25, 2025

Язык: Английский

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Parrot optimizer with multiple search strategies for parameters estimation of proton exchange membrane fuel cells model

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 14, 2025

Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising renewable energy source, generating significant interest in recent years due to their high efficiency, low operating temperature, and durability. Accurately estimating seven unknown parameters the PEMFC electrochemical model is crucial for developing more precise model, thereby improving efficiency performance of systems. For this reason, new optimization method inspired by parrots' (pyrrhura molinaes') behavior, named Parrot Optimizer (PO), introduced here address problem optimal parameter identification ( $$\:{\zeta\:}_{1},\:\:{\zeta\:}_{2},\:{\zeta\:}_{3},\:{\zeta\:}_{4},\:{\lambda\:}_{m},\:{R}_{C},\:\text{a}\text{n}\text{d}\:\beta\:$$ ) models. The estimate these characteristics treated challenging, nonlinear issue that has be addressed with strong technique. paper outlines two improvements basic PO algorithm: first involves employing Opposition-based Learning boost search refine candidate solution generation. second integrates Local Escaping Operator exploration capabilities mitigate risk getting trapped local optima, enhance overall convergence behavior. IPO was rigorously validated through application benchmark functions assess its performance. Three distinct stacks, NedStackPS6, BCS Stack, Ballard Mark V, been used empirically demonstrate efficacy improved optimizing model. Several recognized modeling approaches from literature are comprehensive examination show method's dependability. V units, corresponding SQE values 2.065816 0.012457 0.814325 V. demonstrates 12.87% improvement best measure an 88.37% reduction standard deviation compared PO. results designed approach, including sensitivity analysis, correctly characterizes effectively achieves lowest consistent trajectories.

Язык: Английский

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Efficient identification of photovoltaic cell parameters via Bayesian Neural Network-artificial ecosystem optimization algorithm

Global Energy Interconnection, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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A Sequoia-Ecology-Based Metaheuristic Optimisation Algorithm for Multi-Constraint Engineering Design and UAV Path Planning

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105130 - 105130

Опубликована: Апрель 1, 2025

Язык: Английский

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Exploring the role of negative emission technologies in regional power system planning toward carbon net zero -- A Case Study for the Province of Saskatchewan, Canada

Energy, Год журнала: 2025, Номер unknown, С. 136351 - 136351

Опубликована: Апрель 1, 2025

Язык: Английский

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Random Walk‐Based GOOSE Algorithm for Solving Engineering Structural Design Problems

Engineering Reports, Год журнала: 2025, Номер 7(5)

Опубликована: Апрель 30, 2025

ABSTRACT The proposed Random Walk‐based Improved GOOSE (IGOOSE) search algorithm is a novel population‐based meta‐heuristic inspired by the collective movement patterns of geese and stochastic nature random walks. This includes inherent balance between exploration exploitation integrating walk behavior with local strategies. In this paper, IGOOSE has been rigorously tested across 23 benchmark functions where 13 benchmarks are varying dimensions (10, 30, 50, 100 dimensions). These provide diverse range optimization landscapes, enabling comprehensive evaluation performance under different problem complexities. various parameters such as convergence speed, magnitude solution, robustness for dimensions. Further, applied to optimize eight distinct engineering problems, showcasing its versatility effectiveness in real‐world scenarios. results these evaluations highlight competitive tool, offering promising both standard complex structural problems. Its ability effectively, combined deal positions valuable tool.

Язык: Английский

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Investigation of the critical buckling load of a column with linearly varying moment of inertia using analytical, numerical, and hybrid machine learning approaches

The Journal of Strain Analysis for Engineering Design, Год журнала: 2025, Номер unknown

Опубликована: Май 10, 2025

This study investigates the buckling behavior of columns with variable cross-sections using analytical, numerical, and hybrid machine learning (ML) approaches. Initially, power series method is employed to calculate loads both constant varying under diverse boundary conditions. Then a finite element (FE) analyses are performed obtain results validate by comparing them from method. Once validated, FE model used generate large dataset encompassing wide range cross-sections, lengths, material properties, as per samples obtained through Sobol sampling A ML then developed integrating XGBoost algorithm particle swarm optimization (PSO) technique for hyperparameter tuning. PSO-XGBoost trained predict cross-sections. Its performance input parameters outside training evaluated statistical metrics scatter plots. The demonstrate excellent agreement between analysis method, confirming reliability achieved remarkable predictive accuracy, R 2 values 0.999 0.996 testing datasets, respectively. Furthermore, SHapley Additive exPlanations (SHAP) conducted explore influence interactions on loads, providing valuable insights into model’s interpretability underlying mechanics column buckling.

Язык: Английский

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Multi-objective Optimization of Industrial Batch Balhimycin and Fed-Batch Lysine Biochemical Processes

Process Biochemistry, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

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An Independent Discriminant Network Towards the Identification of Counterfeit Images and Videos

IETE Journal of Research, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Май 28, 2025

Язык: Английский

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