Machine learning tuning by diversity oriented firefly metaheuristics for Industry 4.0

Abstract

The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade-off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer-aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper-parameters and training to deliver a desired result. This paper proposes a swarm intelligence-based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification-based mechanism, has been proposed and applied to both feature selection and hyper-parameter optimization of two machine learning models-XGBoost and extreme learning machine. The proposed approach has been tested on four real-world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT-IoT, and UNSW-NB 15. Achieved results have been compared to the results of eight other cutting-edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics-accuracy, precision, recall, and f1-score.