Improved aquila optimizer and its applications DOI
Runxia Guo,

Jingxu Yi

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Abstract 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.

Язык: Английский

Improved Aquila optimizer and its applications DOI
Runxia Guo,

Jingxu Yi

Cluster Computing, Год журнала: 2025, Номер 28(4)

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

1
Random Walk‐Based GOOSE Algorithm for Solving Engineering Structural Design Problems DOI Creative Commons

S. Mounika,

Himanshu Sharma, A. Krishna

и другие.

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

Процитировано

0
A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications DOI
Sylia Mekhmoukh Taleb, Elham Tahsin Yasin,

Amylia Ait Saadi

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

Процитировано

0
Hybridizing remora and aquila optimizer with dynamic oppositional learning for structural engineering design problems DOI
Megha Varshney, Pravesh Kumar, Laith Abualigah

и другие.

Journal of Computational and Applied Mathematics, Год журнала: 2024, Номер unknown, С. 116475 - 116475

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
Improved aquila optimizer and its applications DOI
Runxia Guo,

Jingxu Yi

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Abstract 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.

Язык: Английский

Процитировано

0