Title: Parameter optimisation of sliding window algorithm based on ensemble multi-objective evolutionary computation

Authors: Guang Li; Jie Wang; Jing Liang; Caitong Yue; Tai-shan Lou

Addresses: School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China; School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, 453003, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Electrical and Information Engineering, Zhengzhou University of Light Industry, 450002, Zhengzhou China

Abstract: The parameters of sliding window algorithm are difficult to determine. Therefore, a sliding window-based method for parameter optimisation of data stream trend anomaly detection algorithm is proposed in this study. This method regards the data stream anomaly detection as a two-objective optimisation problem. Three optimisation algorithms and ensemble strategies were used to obtain the optimal parameter settings of the algorithm. With this strategy, it is no longer difficult to determine the parameters of the data stream trend anomaly detection algorithm based on the sliding window. Through verification of multiple real parameter data in Tarim Oilfield, it could be known that this method could realise the optimal parameter settings, which provides a reference for the parameter setting of the data stream trend anomaly detection algorithm based on sliding window.

Keywords: sliding window; parameter optimisation; anomaly detection; evolutionary computation; ensemble strategy.

DOI: 10.1504/IJBIC.2022.124328

International Journal of Bio-Inspired Computation, 2022 Vol.19 No.4, pp.228 - 237

Accepted: 10 Jan 2021
Published online: 22 Jul 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article