Affiliations: [a] Shaheed Sukhdev College of Business Studies, University of Delhi, New Delhi, India | [b] Department of Computer Science and Information Technology, KIET Institute of Technology, U.P., India | [c] Department of Computer Science, University of Delhi, New Delhi, India
Abstract: Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.
Keywords: Manta ray foraging optimization, process model discovery, bio-inspired optimization, process mining, event log
