Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Moreover, it is a critical determinant in the consumer's decision-making process when considering a smartphone purchase. From the sustainability perspective, it becomes imperative to explore approaches aimed at mitigating the energy consumption of mobile devices, given the significant global consequences arising from the extensive utilisation of billions of smartphones, which imparts a profound environmental impact. Despite the existence of various energy-efficient programming practices within the Android platform, the dominant mobile ecosystem, there remains a need for documented machine learning-based energy prediction algorithms tailored explicitly for mobile app development. Hence, the 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 plays a crucial role in not only identifying suitable learning algorithms and their corresponding parameters but also determining the optimal number of layers and neurons within each layer. To the best of our knowledge, prior studies have yet to employ any metaheuristic algorithm to address all these hyperparameters simultaneously. 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 metaheuristic algorithms, to identify the best algorithm based on accuracy and resistance to missing values. The comprehensive experiments demonstrate that our proposed approach yields significant outcomes for energy consumption prediction.

翻译：能耗是移动应用开发中的根本性问题，对开发者和最终用户均具有重大意义。此外，它还是消费者在考虑购买智能手机时决策过程的关键决定因素。从可持续性角度来看，鉴于数十亿智能手机的广泛使用对全球环境产生的深远影响，探索旨在降低移动设备能耗的方法势在必行。尽管在占据主导地位的移动生态系统Android平台中存在多种节能编程实践，但专门针对移动应用开发的、基于机器学习的能耗预测算法仍有待系统化记录与构建。因此，本研究的主要目标是提出一种新颖的、基于元启发式方法增强的神经网络框架，以实现移动应用开发背景下稳健的能耗预测。此处的元启发式方法不仅用于识别合适的学习算法及其对应参数，还能在确定网络最优层数与每层神经元数量方面发挥关键作用。据我们所知，此前尚无研究采用任何元启发式算法同时处理所有这些超参数。此外，由于访问手机部分功能存在限制，数据集中可能出现缺失值，而本框架能够应对该问题。同时，我们采用包含13种元启发式算法的最优算法选择策略，基于准确率和缺失值抵抗力筛选出最佳算法。综合实验表明，所提方法在能耗预测方面取得了显著成果。