Combining Constraint Solving with Different MOEAs for Configuring Large Software Product Lines: A Case Study

Multi-objective evolutionary algorithm (MOEA) with the constraint solving has been successfully applied to address the configuration optimization problem in software product line (SPL), for example, the state-of-the-art SATIBEA algorithm. However, each different MOEA with special search operator demonstrates the different strength and weakness in terms of optimality and convergence speed. The SATIBEA just combines the SAT (Boolean satisfiability problem) constraint solving with the Indicator-Based Evolutionary Algorithm (IBEA) for evaluating the algorithm performance. In this paper, we propose six hybrid algorithms which combine the SAT solving with different MOEAs. Case study is based on five large-scale, rich-constrained and real-world SPLs. Empirical results demonstrate that SATMOCell algorithm obtains a competitive optimization performance to the state-of-the-art that outperforms the SATIBEA in terms of quality Hypervolume metric for 2 out of 5 SPLs within the same time budget. Moreover, the convergence speed of SATMOCell and SATssNSGA2 is comparable after 10min terminal times. Particularly, the Hypervolume value of SATssNSGA2 reports the average improvement of 1.33% after 20min terminal times.