Deficiencies of the whale optimization algorithm and its validation method

In the context of urgent requirements for efficient metaheuristics, the whale optimization algorithm (WOA) is tailored for tackling sophisticated optimization problems and has gained extensive momentum since its emergence. By drawing inspiration from the living habits of whales involving bubble-net attacking, encircling prey and discovering prey, WOA seems powerful to handle challenging problems owing to its unique mechanism. Nevertheless, in this work, through comparative experiments on several standard benchmarks and their shifted versions, we discuss the design flaws of WOA and exhibit the related cause analysis. Furthermore, we employ a useful validation method to test the deficiencies of WOA. Simulation outcomes suggest that WOA integrates a center-bias operator, which makes the algorithm shift-variant and limits its performances to tackle shifted benchmarks.