Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering
The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific si...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/1/54 |
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| author | Megha Varshney Pravesh Kumar Musrrat Ali Yonis Gulzar |
| author_facet | Megha Varshney Pravesh Kumar Musrrat Ali Yonis Gulzar |
| author_sort | Megha Varshney |
| collection | DOAJ |
| description | The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces. |
| first_indexed | 2024-03-08T11:04:34Z |
| format | Article |
| id | doaj.art-a2f3af26be8f42fa901cee9795544166 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T11:04:34Z |
| publishDate | 2024-01-01 |
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| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-a2f3af26be8f42fa901cee97955441662024-01-26T15:16:51ZengMDPI AGBiomimetics2313-76732024-01-01915410.3390/biomimetics9010054Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural EngineeringMegha Varshney0Pravesh Kumar1Musrrat Ali2Yonis Gulzar3Rajkiya Engineering College, Dr. APJ Abdul Kalam Kalam Technical University, Bijnor 246725, IndiaRajkiya Engineering College, Dr. APJ Abdul Kalam Kalam Technical University, Bijnor 246725, IndiaDepartment of Basic Sciences, General Administration of Preparatory Year, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi ArabiaThe Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces.https://www.mdpi.com/2313-7673/9/1/54Aquila Optimizergrey wolf optimizationquasi-opposition-based learningreal-world engineering problems |
| spellingShingle | Megha Varshney Pravesh Kumar Musrrat Ali Yonis Gulzar Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering Biomimetics Aquila Optimizer grey wolf optimization quasi-opposition-based learning real-world engineering problems |
| title | Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering |
| title_full | Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering |
| title_fullStr | Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering |
| title_full_unstemmed | Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering |
| title_short | Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering |
| title_sort | using the grey wolf aquila synergistic algorithm for design problems in structural engineering |
| topic | Aquila Optimizer grey wolf optimization quasi-opposition-based learning real-world engineering problems |
| url | https://www.mdpi.com/2313-7673/9/1/54 |
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