Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems
This work provides practical guidance for researchers and practitioners in optimization on choosing between metaheuristic and deterministic techniques based on evaluation cost constraints.
The paper compared nature-inspired and deterministic derivative-free optimization methods on five benchmark sets, finding that nature-inspired methods perform significantly better when function evaluations are cheap, while deterministic methods are more consistent when evaluations are costly.
In the field of derivative-free optimization, both of its main branches, the deterministic and nature-inspired techniques, experienced in recent years substantial advancement. In this paper, we provide an extensive computational comparison of selected methods from each of these branches. The chosen representatives were either standard and well-utilized methods, or the best-performing methods from recent numerical comparisons. The computational comparison was performed on five different benchmark sets and the results were analyzed in terms of performance, time complexity, and convergence properties of the selected methods. The results showed that, when dealing with situations where the objective function evaluations are relatively cheap, the nature-inspired methods have a significantly better performance than their deterministic counterparts. However, in situations when the function evaluations are costly or otherwise prohibited, the deterministic methods might provide more consistent and overall better results.