Improved Aquila optimizer and its applications

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

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

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(5)

Published: April 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.

Language: Английский

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A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Language: Английский

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Improved aquila optimizer and its applications

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

The optimizer is a key component in model training, embodied speeding up improving stability, and seeking globally optimal solutions. Aquila (AO) an intelligent algorithm that excels searching for values, which simulates the hunting habits of North American eagles. However, classical AO suffers from low convergence accuracy tendency to fall into local optima when handling complex tasks. This paper introduces novel improved (I-AO) population-based meta-inspired domains, enhancing global optimization reliability. Initially, we introduce spatial configuration differences distance angle replace original iteration-based criteria. approach enhances optimizer's speed robustness, enabling quick adaptation dynamic changes. Next, random walk update (I-RWU), stochastic correction (I-SCU), weight (I-DWU) strategies. These strategies enhance search diversity balance exploration exploitation, avoiding optima. Thus, I-AO achieves higher computational efficiency greater potential convergence. Furthermore, performance evaluated using well-known CEC2017 CEC2019 benchmark functions. Additionally, engineering problem bearing’s RUL prediction air turbine starter (ATS), test bed data civil aircraft bearings, illustrates algorithm's generalizability. superior capability proposed demonstrated through corresponding experiments.

Language: Английский

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Hybridizing remora and aquila optimizer with dynamic oppositional learning for structural engineering design problems

Journal of Computational and Applied Mathematics, Journal Year: 2024, Volume and Issue: unknown, P. 116475 - 116475

Published: Dec. 1, 2024

Language: Английский

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