A novel multi objective grey wolf optimization fuzzy miner for process discovery: Incorporating robustness and explainability in model evaluation

Abstract

Process mining provides methodologies for analyzing, monitoring, and improving processes based on event logs. This study introduces Fuzzy Multi-Objective Grey Wolf Optimization (Fuzzy MOGWO), which integrates fuzzy modeling with a multi-criteria metaheuristic optimization approach. The proposed framework simultaneously optimizes six metrics: Fitness, Precision, Generalization, Simplicity, Robustness, and Explainability with the latter two newly proposed to evaluate noise resilience and analyst interpretability. A normalized scoring mechanism, based on the L2 norm of all metrics, ensures balanced evaluation across objectives. Fuzzy MOGWO is benchmarked against Alpha Miner, Inductive Miner, and Fuzzy Miner using 10 synthetic noise-free logs, 10 synthetic noisy logs with 5–20% injected noise, and 3 real-world logs. Under noise-free conditions, it achieved a normalized score of 0.329, surpassing the best baseline (0.288) by 14.24%. In noisy environments, its score (0.440) exceeded the top competitor (0.378) by 16.40%. On real-world logs, it outperformed competitors in 4 out of 6 metrics, compared to 2 out of 6 for the PSO-based miner. These results demonstrate substantially improved effectiveness, robust performance in the presence of noise, and enhanced interpretability, establishing Fuzzy MOGWO as a comprehensive and reliable solution for challenging process discovery tasks.