A Metaheuristic-based Machine Learning Approach for Energy Prediction in Mobile App Development

TL;DR

This paper introduces a metaheuristic-enhanced machine learning method using HGBC for accurate energy prediction in mobile app development, aiming to reduce energy consumption and improve sustainability.

Contribution

It proposes a novel metaheuristic approach to optimize feature selection and hyper-parameters for energy prediction in mobile apps, enhancing existing methods.

Findings

L-SHADE algorithm outperforms other search strategies.

The approach reduces feature set size without performance loss.

Significant improvement in energy consumption prediction accuracy.

Abstract

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.