Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Main objective of this research is to propose a novel neural network-based framework, enhanced by a metaheuristic approach, to achieve robust energy prediction in the context of mobile app development. The metaheuristic approach here aims to achieve two goals: 1) identifying suitable learning algorithms and their corresponding hyperparameters, and 2) determining the optimal number of layers and neurons within each layer. Moreover, due to limitations in accessing certain aspects of a mobile phone, there might be missing data in the data set, and the proposed framework can handle this. In addition, we conducted an optimal algorithm selection strategy, employing 13 base and advanced metaheuristic algorithms, to identify the best algorithm based on accuracy and resistance to missing values. The representation in our proposed metaheuristic algorithm is variable-size, meaning that the length of the candidate solutions changes over time. We compared the algorithms based on the architecture found by each algorithm at different levels of missing values, accuracy, F-measure, and stability analysis. Additionally, we conducted a Wilcoxon signed-rank test for statistical comparison of the results. The extensive experiments show that our proposed approach significantly improves energy consumption prediction. Particularly, the JADE algorithm, a variant of Differential Evolution (DE), DE, and the Covariance Matrix Adaptation Evolution Strategy deliver superior results under various conditions and across different missing value levels.

翻译：能耗是移动应用开发中的一个核心问题，对开发者和终端用户都具有重要意义。本研究的主要目标是提出一种新颖的基于神经网络的框架，并通过元启发式方法进行增强，以在移动应用开发背景下实现鲁棒的能耗预测。此处的元启发式方法旨在实现两个目标：1）识别合适的学习算法及其对应的超参数，2）确定最佳的层数以及每层中的神经元数量。此外，由于访问手机某些方面的限制，数据集中可能存在缺失数据，而所提出的框架能够处理这种情况。另外，我们实施了一种最优算法选择策略，采用了13种基础和先进的元启发式算法，以基于准确性和对缺失值的鲁棒性来识别最佳算法。我们提出的元启发式算法中的表示是可变长度的，这意味着候选解的长度会随时间变化。我们基于每种算法在不同缺失值水平下找到的架构、准确性、F-measure和稳定性分析来比较这些算法。此外，我们还进行了Wilcoxon符号秩检验以对结果进行统计比较。大量实验表明，我们提出的方法显著改善了能耗预测。特别是，差分进化（DE）的变体JADE算法、DE算法以及协方差矩阵自适应进化策略在各种条件下以及不同缺失值水平下均能提供优异的结果。