Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
This addresses the issue of algorithm proliferation in optimization for researchers and practitioners, but it is incremental as it applies existing statistical methods to a new context.
The paper tackles the problem of distinguishing meaningful innovations in metaheuristic optimization algorithms by using statistical tests to compare their search behaviors, analyzing 114 algorithms from the MEALPY library to identify those with similar behaviors.
The field of numerical optimization has recently seen a surge in the development of "novel" metaheuristic algorithms, inspired by metaphors derived from natural or human-made processes, which have been widely criticized for obscuring meaningful innovations and failing to distinguish themselves from existing approaches. Aiming to address these concerns, we investigate the applicability of statistical tests for comparing algorithms based on their search behavior. We utilize the cross-match statistical test to compare multivariate distributions and assess the solutions produced by 114 algorithms from the MEALPY library. These findings are incorporated into an empirical analysis aiming to identify algorithms with similar search behaviors.