A Systematic and Meta-analysis Survey of Whale Optimization Algorithm

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 optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.