A Metaheuristic-based Machine Learning Approach for Energy Prediction in
  Mobile App Development
Seyed Jalaleddin Mousavirad Luís A. Alexandre
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.