Aquila optimizer: review, results and applications DOI
Laith Abualigah,

Batool Sbenaty,

Abiodun M. Ikotun

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 89 - 103

Published: Jan. 1, 2024

Language: Английский

A synergy of an evolutionary algorithm with slime mould algorithm through series and parallel construction for improving global optimization and conventional design problem DOI
Sumika Chauhan, Govind Vashishtha

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 118, P. 105650 - 105650

Published: Dec. 1, 2022

Language: Английский

Citations

27

A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators DOI Creative Commons
Ahmed A. Ewees, Zakariya Yahya Algamal, Laith Abualigah

et al.

Mathematics, Journal Year: 2022, Volume and Issue: 10(8), P. 1273 - 1273

Published: April 12, 2022

Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although AO has significant performance in various problems, like other algorithms, suffers from certain limitations its search mechanism, such as local optima stagnation and convergence speed. This is general problem that faces almost all which can be solved by enhancing process of an optimizer using assistant tool, hybridizing with another or applying techniques boost capability optimizer. Following this concept address critical problem, paper, we present alternative version alleviate shortcomings traditional one. The main idea improved (IAO) use strategy Whale Optimization Algorithm (WOA) AO. Thus, IAO benefits advantages WOA, it avoids well losing solutions diversity through process. Moreover, apply algorithm feature selection technique benchmark functions. More so, tested extensive experimental comparisons WOA several well-known optimizers used techniques, particle swarm (PSO), differential evaluation (DE), mouth flame (MFO), firefly algorithm, genetic (GA). outcomes confirmed operators impact on performance. Thus combined obtained better results compared optimizers.

Language: Английский

Citations

23

Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods DOI
Ali Mohammadi, Farid Sheikholeslam, Seyedali Mirjalili

et al.

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(1), P. 331 - 389

Published: Aug. 29, 2022

Language: Английский

Citations

20

Hybrid aquila arithmetic optimization based ANFIS for harmonic mitigation in grid connected solar fed distributed energy systems DOI Open Access

Jenner Zahariah,

Tibbie Pon Symon V. A.

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 226, P. 109898 - 109898

Published: Oct. 24, 2023

Language: Английский

Citations

12

Aquila optimizer: review, results and applications DOI
Laith Abualigah,

Batool Sbenaty,

Abiodun M. Ikotun

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 89 - 103

Published: Jan. 1, 2024

Language: Английский

Citations

4