今天（2017/12/19）我们的论文Configuring Software Product Lines by Combining Many-objective Optimization and SAT Solvers 被ACM TOSEM 正式接受！
本文提出采用高维多目标演化算法结合SAT求解器的方式，解决大规模软件产品线配置问题，取得了很好的效果。这是高维多目标演化算法的一次成功应用。
TOSEM 是软件工程领域两个CCF-A期刊之一。每年出版4期，每期论文不到10篇（有时候就3-5篇）。每年仅发表30篇论文左右。http://blog.csdn.net/lovelion/article/details/19019347
以下是论文摘要：
A feature model is a compact representation of the information of all possible products from software product lines. The optimal feature selection involves the simultaneous optimization of multiple (usually more than three) objectives in a large and highly constrained search space. By combining our previous work on many-objective evolutionary algorithm (i.e., VaEA) with two different satisfiability (SAT) solvers, this paper proposes a new approach named SATVaEA for handling the optimal feature selection problem. In SATVaEA, a feature model is simplified with the number of both features and constraints being reduced greatly. We enhance the search of VaEA by using two SAT solvers: One is a stochastic local search based SAT solver that can quickly repair infeasible configurations, while the other is a conflict-driven clause learning SAT solver that is introduced to generate diversified products. We evaluate SATVaEA on 21 feature models with up to 62,482 features, including two models with realistic values for feature attributes. The experimental results are promising, with SATVaEA returning 100% valid products on almost all the feature models. For models with more than 10,000 features, the search in SATVaEA takes only a few minutes. Concerning both the effectiveness and efficiency, SATVaEA significantly outperforms other state-of-the-art algorithms.
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