A Systematic and Meta-analysis Survey of Whale Optimization Algorithm
Hardi M. Mohammed (1,3), Shahla U. Umar (1,4), Tarik A. Rashid (2), ((1) Technical College of Informatics, Sulaimani Polytechnic University,, Sulaimani, KRG, Iraq. (2) Computer Science, Engineering, University of, Kurdistan Hewler (UKH), Erbil, KRG

TL;DR
This paper provides a comprehensive systematic and meta-analysis survey of the Whale Optimization Algorithm (WOA), highlighting its characteristics, modifications, hybridizations, and superior performance in various optimization problems.
Contribution
It is the first extensive survey on WOA, analyzing its performance, modifications, hybridizations, and proposing a new hybrid WOA-BAT algorithm with superior results.
Findings
WOA outperforms many algorithms in convergence speed.
Modifications and hybridizations improve WOA's performance.
The proposed WOA-BAT hybrid outperforms standard WOA in benchmark tests.
Abstract
Whale Optimization Algorithm (WOA) is a nature-inspired meta-heuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC, PSO, etc.Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Robotic Path Planning Algorithms · Vehicle Routing Optimization Methods
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
