  Software systems continuously evolve due to new functionalities,
requirements, or maintenance activities. In the context of software evolution,
software refactoring has gained a strategic relevance. The space of possible
software refactoring is usually very large, as it is given by the combinations
of different refactoring actions that can produce software system alternatives.
Multi-objective algorithms have shown the ability to discover alternatives by
pursuing different objectives simultaneously. Performance of such algorithms in
the context of software model refactoring is of paramount importance.
Therefore, in this paper, we conduct a performance analysis of three genetic
algorithms to compare them in terms of performance and quality of solutions.
Our results show that there are significant differences in performance among
the algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II shows
the least memory usage).
