An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail.

中文翻译：

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多目标优化算法中具有局部搜索和精英继承机制的变异：以软件产品线为例

解决软件产品线 (SPL) 中的配置优化问题 (COP) 的一种有效方法是部署多目标进化算法，例如最先进的 SATIBEA。本文提出了一种改进的混合算法，称为SATIBEA-LSSF，以进一步提高SATIBEA的算法性能，该算法由多子生成策略、局部搜索的增强变异策略和精英继承机制组成。相同案例研究的经验结果表明，我们的算法在质量超体积指标和收敛速度方面明显优于五分之四的 SPL 的最新技术。为了验证我们算法的有效性和鲁棒性，讨论了参数敏感性分析，并详细报告了三个观察结果。