Development and Applications of Augmented Whale Optimization Algorithm
Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some...
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MDPI AG
2022-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/12/2076 |
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| author | Khalid Abdulaziz Alnowibet Shalini Shekhawat Akash Saxena Karam M. Sallam Ali Wagdy Mohamed |
| author_facet | Khalid Abdulaziz Alnowibet Shalini Shekhawat Akash Saxena Karam M. Sallam Ali Wagdy Mohamed |
| author_sort | Khalid Abdulaziz Alnowibet |
| collection | DOAJ |
| description | Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance. |
| first_indexed | 2024-03-09T23:09:18Z |
| format | Article |
| id | doaj.art-ed9a8c39ffea416c9e042d5c7b90a3a2 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:09:18Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ed9a8c39ffea416c9e042d5c7b90a3a22023-11-23T17:49:21ZengMDPI AGMathematics2227-73902022-06-011012207610.3390/math10122076Development and Applications of Augmented Whale Optimization AlgorithmKhalid Abdulaziz Alnowibet0Shalini Shekhawat1Akash Saxena2Karam M. Sallam3Ali Wagdy Mohamed4Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi ArabiaSwami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, Rajasthan, IndiaSwami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, Rajasthan, IndiaSchool of IT and Systems, University of Canberra, Canberra, ACT 2601, AustraliaOperations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, EgyptMetaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance.https://www.mdpi.com/2227-7390/10/12/2076metaheuristic algorithmsWhale Optimization Algorithm |
| spellingShingle | Khalid Abdulaziz Alnowibet Shalini Shekhawat Akash Saxena Karam M. Sallam Ali Wagdy Mohamed Development and Applications of Augmented Whale Optimization Algorithm Mathematics metaheuristic algorithms Whale Optimization Algorithm |
| title | Development and Applications of Augmented Whale Optimization Algorithm |
| title_full | Development and Applications of Augmented Whale Optimization Algorithm |
| title_fullStr | Development and Applications of Augmented Whale Optimization Algorithm |
| title_full_unstemmed | Development and Applications of Augmented Whale Optimization Algorithm |
| title_short | Development and Applications of Augmented Whale Optimization Algorithm |
| title_sort | development and applications of augmented whale optimization algorithm |
| topic | metaheuristic algorithms Whale Optimization Algorithm |
| url | https://www.mdpi.com/2227-7390/10/12/2076 |
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