今天（2017/12/19）我們的論文Configuring Software Product Lines by Combining Many-objective Optimization and SAT Solvers 被ACM TOSEM 正式接受！

本文提出采用高維多目標(biāo)演化算法結(jié)合SAT求解器的方式，解決大規(guī)模軟件產(chǎn)品線配置問(wèn)題，取得了很好的效果。這是高維多目標(biāo)演化算法的一次成功應(yīng)用。

TOSEM 是軟件工程領(lǐng)域兩個(gè)CCF-A期刊之一。每年出版4期，每期論文不到10篇（有時(shí)候就3-5篇）。每年僅發(fā)表30篇論文左右。http://blog.csdn.net/lovelion/article/details/19019347

以下是論文摘要：

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.