  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.

Neural and Evolutionary Computing