TY - JOUR
T1 - Development and Applications of Augmented Whale Optimization Algorithm
AU - Alnowibet, Khalid Abdulaziz
AU - Shekhawat, Shalini
AU - Saxena, Akash
AU - Sallam, Karam M.
AU - Mohamed, Ali Wagdy
N1 - Funding Information:
Funding: The research is funded by Researchers Supporting Program at King Saud University, (RSP-2021/305).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6/15
Y1 - 2022/6/15
N2 - Metaheuristics are proven solutions for complex optimization problems. Recently, bioinspired 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.
AB - Metaheuristics are proven solutions for complex optimization problems. Recently, bioinspired 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.
KW - metaheuristic algorithms
KW - Whale Optimization Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85132711180&partnerID=8YFLogxK
U2 - 10.3390/math10122076
DO - 10.3390/math10122076
M3 - Article
AN - SCOPUS:85132711180
SN - 2227-7390
VL - 10
SP - 1
EP - 33
JO - Mathematics
JF - Mathematics
IS - 12
M1 - 2076
ER -