Development and Applications of Augmented Whale Optimization Algorithm

Khalid Abdulaziz Alnowibet, Shalini Shekhawat, Akash Saxena, Karam M. Sallam and Ali Wagdy Mohamed
Additional contact information
Khalid Abdulaziz Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Shalini Shekhawat: Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, Rajasthan, India
Akash Saxena: Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, Rajasthan, India
Karam M. Sallam: School of IT and Systems, University of Canberra, Canberra, ACT 2601, Australia
Ali Wagdy Mohamed: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Mathematics, 2022, vol. 10, issue 12, 1-33

Abstract: 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.

Keywords: metaheuristic algorithms; Whale Optimization Algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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