SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines

摘要

A key challenge to software product line engineering is to explore a huge space of various products and to find optimal or near-optimal solutions that satisfy all predefined constraints and balance multiple often competing objectives. To address this challenge, we propose a hybrid multi-objective optimization algorithm called SMTIBEA that combines the indicator-based evolutionary algorithm (IBEA) with the satisfiability modulo theories (SMT) solving. We evaluated the proposed algorithm on five large, constrained, real-world SPLs. Compared to the state-of-the-art, our approach significantly extends the expressiveness of constraints and simultaneously achieves a comparable performance. Furthermore, we investigate the performance influence of the SMT solving on two evolutionary operators of the IBEA.