  It is essential that all algorithms are exhaustively, somewhat, and
intelligently evaluated. Nonetheless, evaluating the effectiveness of
optimization algorithms equitably and fairly is not an easy process for various
reasons. Choosing and initializing essential parameters, such as the size
issues of the search area for each method and the number of iterations required
to reduce the issues, might be particularly challenging. As a result, this
chapter aims to contrast the Whale Optimization Algorithm (WOA) with the most
recent algorithms on a selected set of benchmark problems with varying
benchmark function hardness scores and initial control parameters comparable
problem dimensions and search space. When solving a wide range of numerical
optimization problems with varying difficulty scores, dimensions, and search
areas, the experimental findings suggest that WOA may be statistically superior
or inferior to the preceding algorithms referencing convergence speed, running
time, and memory utilization.
