# Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis

arXiv: https://arxiv.org/abs/2507.01668 · HTML: https://mlsanity.com/paper/2507.01668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics](https://mlsanity.com/paper/2111.04077/) (2021)
- [Large-scale Benchmarking of Metaphor-based Optimization Heuristics](https://mlsanity.com/paper/2402.09800/) (2024)

## Built on by (most cited first, 1 of 1)

- [MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning](https://mlsanity.com/paper/2607.01539/) (2026)

## Abstract (arXiv)

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.