Bald eagle search algorithm: a comprehensive review with its variants and applications DOI Creative Commons
M.A. El‐Shorbagy, Anas Bouaouda, Hossam A. Nabwey

et al.

Systems Science & Control Engineering, Journal Year: 2024, Volume and Issue: 12(1)

Published: Aug. 1, 2024

Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global local searches during optimization, BES effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers comprehensive review research on BES. Beginning with an introduction BES's natural inspiration conceptual framework, it explores modifications, hybridizations, applications domains. Then, critical evaluation performance provided, offering update effectiveness compared recently published algorithms. Furthermore, presents meta-analysis developments outlines potential future directions. As swarm-inspired algorithms become increasingly important tackling complex problems, this study valuable resource for researchers aiming understand algorithms, mainly focusing comprehensively. It investigates evolution, exploring solving intricate fields.

Language: Английский

Beluga whale optimization: A novel nature-inspired metaheuristic algorithm DOI
Changting Zhong, Gang Li, Zeng Meng

et al.

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 251, P. 109215 - 109215

Published: June 9, 2022

Language: Английский

Citations

538
A survey on river water quality modelling using artificial intelligence models: 2000–2020 DOI
Tiyasha Tiyasha, Tran Minh Tung, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al.

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 585, P. 124670 - 124670

Published: Feb. 14, 2020

Language: Английский

Citations

537
Political Optimizer: A novel socio-inspired meta-heuristic for global optimization DOI
Qamar Askari, Irfan Younas,

Mehreen Saeed

et al.

Knowledge-Based Systems, Journal Year: 2020, Volume and Issue: 195, P. 105709 - 105709

Published: March 8, 2020

Language: Английский

Citations

473
Heap-based optimizer inspired by corporate rank hierarchy for global optimization DOI
Qamar Askari,

Mehreen Saeed,

Irfan Younas

et al.

Expert Systems with Applications, Journal Year: 2020, Volume and Issue: 161, P. 113702 - 113702

Published: July 18, 2020

Language: Английский

Citations

317
An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions DOI
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Chemosphere, Journal Year: 2021, Volume and Issue: 277, P. 130126 - 130126

Published: March 19, 2021

Language: Английский

Citations

268
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges DOI Open Access
Kanchan Rajwar, Kusum Deep, Swagatam Das

et al.

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(11), P. 13187 - 13257

Published: April 9, 2023

Language: Английский

Citations

254
Metaheuristics: a comprehensive overview and classification along with bibliometric analysis DOI
Absalom E. Ezugwu, Amit K. Shukla, Rahul Nath

et al.

Artificial Intelligence Review, Journal Year: 2021, Volume and Issue: 54(6), P. 4237 - 4316

Published: March 16, 2021

Language: Английский

Citations

227
Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems DOI
Mohamed Abdel‐Basset, Doaa El-Shahat, Mohammed Jameel

et al.

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(9), P. 9329 - 9400

Published: Jan. 30, 2023

Language: Английский

Citations

124
Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems DOI
Mohamed Abdel‐Basset, Doaa El-Shahat, Mohammed Jameel

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2022, Volume and Issue: 403, P. 115652 - 115652

Published: Nov. 4, 2022

Language: Английский

Citations

120
A survey of recently developed metaheuristics and their comparative analysis DOI Creative Commons
Abdulaziz Alorf

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 117, P. 105622 - 105622

Published: Nov. 25, 2022

The aim of this study was to gather, discuss, and compare recently developed metaheuristics understand the pace development in field make some recommendations for research community practitioners. By thoroughly comprehensively searching literature narrowing search results, we created with a list 57 novel metaheuristic algorithms. Based on availability source code, reviewed analysed optimization capability 26 these algorithms through series experiments. We also evaluated exploitation exploration capabilities by using 50 unimodal functions multimodal functions, respectively. In addition, assessed balance 29 shifted, rotated, composite, hybrid CEC-BC-2017 benchmark functions. Moreover, applicability four real-world constrained engineering problems. To rank algorithms, performed nonparametric statistical test, Friedman mean test. results declared that GBO, PO, MRFO have better capabilities. found MPA, FBI, HBO be most balanced. Finally, based problems, HBO, MA are suitable. Collectively, confidently recommend

Language: Английский

Citations

74