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

TL;DR

This paper introduces METAFOR, a modular software framework for hybrid metaheuristics, which, combined with an automatic configuration tool, efficiently designs and evaluates hybrid algorithms for continuous optimization problems, outperforming single-approach methods.

Contribution

The paper presents METAFOR, a flexible framework that automates the creation and testing of hybrid metaheuristics, integrating multiple optimization strategies and local search, with demonstrated superior performance.

Findings

Automatically generated hybrids outperform single-approach algorithms across diverse problems.

Hybridization strategies vary in effectiveness depending on problem class.

Insights into component contributions and validation strategies improve hybrid algorithm design.

Abstract

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.