# Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems

arXiv: https://arxiv.org/abs/2212.06875 · HTML: https://mlsanity.com/paper/2212.06875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 70 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge](https://mlsanity.com/paper/1904.10535/) (2019)
- [Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)](https://mlsanity.com/paper/2002.08136/) (2020)
- [Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review](https://mlsanity.com/paper/1806.04563/) (2018)
- [A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms](https://mlsanity.com/paper/2004.09969/) (2020)
- [DIRECTGO: A new DIRECT-type MATLAB toolbox for derivative-free global optimization](https://mlsanity.com/paper/2107.02205/) (2021)

## Abstract (arXiv)

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.