# METAFOR: A Hybrid Metaheuristics Software Framework for Single-Objective Continuous Optimization Problems

arXiv: https://arxiv.org/abs/2502.11225 · HTML: https://mlsanity.com/paper/2502.11225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 89 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules](https://mlsanity.com/paper/2102.12905/) (2021)
- [A Modular Hybridization of Particle Swarm Optimization and Differential Evolution](https://mlsanity.com/paper/2006.11886/) (2020)

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

- [Quantifying the Impact of Modules and Their Interactions in the PSO-X Framework](https://mlsanity.com/paper/2601.04100/) (2026)

## Abstract (arXiv)

Hybrid metaheuristics are powerful techniques for solving difficult optimization problems that exploit the strengths of different approaches in a single implementation. For algorithm designers, however, creating hybrid metaheuristic implementations has become increasingly challenging due to the vast number of design options available in the literature and the fact that they often rely on their knowledge and intuition to come up with new algorithm designs. In this paper, we propose a modular metaheuristic software framework, called METAFOR, that can be coupled with an automatic algorithm configuration tool to automatically design hybrid metaheuristics. METAFOR is specifically designed to hybridize Particle Swarm Optimization, Differential Evolution and Covariance Matrix Adaptation-Evolution Strategy, and includes a local search module that allows their execution to be interleaved with a subordinate local search. We use the configuration tool irace to automatically generate 17 different metaheuristic implementations and evaluate their performance on a diverse set of continuous optimization problems. Our results show that, across all the considered problem classes, automatically generated hybrid implementations are able to outperform configured single-approach implementations, while these latter offer advantages on specific classes of functions. We provide useful insights on the type of hybridization that works best for specific problem classes, the algorithm components that contribute to the performance of the algorithms, and the advantages and disadvantages of two well-known instance separation strategies, creating stratified training set using a fix percentage and leave-one-class-out cross-validation.