METAFOR: A Hybrid Metaheuristics Software Framework for Single-Objective Continuous Optimization Problems
This work addresses the difficulty for algorithm designers in creating effective hybrid metaheuristics, offering an automated solution that improves performance on continuous optimization problems, though it is incremental as it builds on existing metaheuristic methods.
The authors tackled the challenge of designing hybrid metaheuristics for continuous optimization by proposing METAFOR, a modular software framework that automatically generates hybrid implementations using tools like irace, and found that these automatically generated hybrids outperformed single-approach implementations across various problem classes.
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.