MSGWO-MKL-SVM: A Missing Link Prediction Method for UAV Swarm Network Based on Time Series

Mingyu Nan, Yifan Zhu, Jie Zhang, Tao Wang and Xin Zhou
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Mingyu Nan: College of System Engineering, National University of Defense Technology, Changsha 410000, China
Yifan Zhu: College of System Engineering, National University of Defense Technology, Changsha 410000, China
Jie Zhang: College of System Engineering, National University of Defense Technology, Changsha 410000, China
Tao Wang: College of System Engineering, National University of Defense Technology, Changsha 410000, China
Xin Zhou: College of System Engineering, National University of Defense Technology, Changsha 410000, China

Mathematics, 2022, vol. 10, issue 14, 1-29

Abstract: Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average.

Keywords: UAV swarm; missing links prediction; time series data; multiple kernel learning; multi-objective optimization; grey wolf optimizer; support vector machine; complex network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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