Energy consumption plays a vital role in mobile App development for developers and end-users, and it is considered one of the most crucial factors for purchasing a smartphone. In addition, in terms of sustainability, it is essential to find methods to reduce the energy consumption of mobile devices since the extensive use of billions of smartphones worldwide significantly impacts the environment. Despite the existence of several energy-efficient programming practices in Android, the leading mobile ecosystem, machine learning-based energy prediction algorithms for mobile App development have yet to be reported. Therefore, this paper proposes a histogram-based gradient boosting classification machine (HGBC), boosted by a metaheuristic approach, for energy prediction in mobile App development. Our metaheuristic approach is responsible for two issues. First, it finds redundant and irrelevant features without any noticeable change in performance. Second, it performs a hyper-parameter tuning for the HGBC algorithm. Since our proposed metaheuristic approach is algorithm-independent, we selected 12 algorithms for the search strategy to find the optimal search algorithm. Our finding shows that a success-history-based parameter adaption for differential evolution with linear population size (L-SHADE) offers the best performance. It can improve performance and decrease the number of features effectively. Our extensive set of experiments clearly shows that our proposed approach can provide significant results for energy consumption prediction.

翻译：能耗在移动应用开发中对开发者和终端用户至关重要，且被视为购买智能手机时最关键的考量因素之一。从可持续性角度看，由于全球数十亿智能手机的广泛使用对环境产生显著影响，寻找降低移动设备能耗的方法尤为重要。尽管作为领先移动生态系统的安卓系统已存在多种节能编程实践，但基于机器学习的移动应用开发能耗预测算法仍未见报道。为此，本文提出一种基于直方图的梯度提升分类机（HGBC），通过元启发式方法增强，用于移动应用开发中的能耗预测。我们的元启发式方法负责解决两个问题：其一，在不显著影响性能的前提下识别冗余和非相关特征；其二，对HGBC算法进行超参数调优。由于所提元启发式算法具有算法独立性，我们选取了12种算法作为搜索策略以寻找最优搜索算法。研究结果表明，基于成功历史参数自适应的线性种群规模差分进化算法（L-SHADE）性能最优，能有效提升预测精度并减少特征数量。通过大量实验验证，本文所提方法在能耗预测中可取得显著成果。