An Improved Chaotic Game Optimization Algorithm and Its Application in Air Quality Prediction

Axioms, Год журнала: 2025, Номер 14(4), С. 235 - 235

Опубликована: Март 21, 2025

Air pollution poses significant threats to public health and ecological sustainability, necessitating precise air quality prediction facilitate timely preventive measures policymaking. Although Long Short-Term Memory (LSTM) networks demonstrate effectiveness in prediction, their performance critically depends on appropriate hyperparameter configuration. Traditional manual parameter tuning methods prove inefficient prone suboptimal solutions. While conventional swarm intelligence algorithms have been proved be effective optimizing the hyperparameters of LSTM models, they still face challenges accuracy model generalizability. To address these limitations, this study proposes an improved chaotic game optimization (ICGO) algorithm incorporating multiple improvement strategies, subsequently developing ICGO-LSTM hybrid for Chengdu’s prediction. The experimental validation comprises two phases: First, comprehensive benchmarking 23 mathematical functions reveals that proposed ICGO achieves superior mean values across all test optimal variance metrics 22 functions, demonstrating enhanced global convergence capability algorithmic robustness. Second, comparative analysis with seven swarm-optimized models six machine learning benchmarks dataset shows model’s performance. Extensive evaluations show minimal error metrics, MAE = 3.2865, MAPE 0.720%, RMSE 4.8089, along exceptional coefficient determination (R2 0.98512). These results indicate significantly outperforms predictive reliability, suggesting substantial practical implications urban environmental management.

Язык: Английский

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An efficient enhanced exponential distribution optimizer: applications in global, engineering, and combinatorial optimization problems

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 13, 2025

Язык: Английский

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Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4530 - 4530

Опубликована: Апрель 19, 2025

The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings low exploratory and population diversity are increasingly exposed. A modified (M-GWO) proposed to tackle these weaknesses GWO. M-GWO introduces mutation operators different location-update strategies, achieving balance between exploration development. experiment validated performance using CEC2017 benchmark function compared results five other advanced algorithms: Improved (IGWO), GWO, Whale Optimization Algorithm (WOA), Dung Beetle (DBO), Harris Hawks (HHO). indicate better than competitor algorithms on all 29 functions in dimensions 30 50, except 26 dimension 28 50. Compared algorithms, most effective algorithm, overall effectiveness 96.5%. In addition, order show value practical engineering field, used optimize PI controller parameters current loop permanent magnet synchronous motor (PMSM) system. By designing parameter optimization scheme based M-GWO, fluctuation q-axis d-axis reduced. designed reduces around −2~1 −2~2 A. comparing current-tracking errors under validity optimized proved.

Язык: Английский

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Enhanced Global Optimization Using a Novel Hybrid Sine Cosine-Gazelle Algorithm with Brownian Motion and Lévy Flight Mechanisms

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Май 8, 2025

Язык: Английский

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A comparative performance of different Type-1 tournament based metaheuristic algorithms in solving engineering beam design optimization problems and structural engineering design problems

Soft Computing, Год журнала: 2025, Номер unknown

Опубликована: Май 13, 2025

Язык: Английский

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