Multi-objective Binary Differential Approach with Parameter Tuning for Discovering Business Process Models: MoD-ProM

TL;DR

This paper introduces a multi-objective binary differential evolution method with parameter tuning for process discovery, producing diverse, high-quality business process models that outperform or match existing algorithms.

Contribution

It presents a novel multi-objective evolutionary approach with parameter tuning for discovering multiple candidate process models, improving diversity and quality over traditional methods.

Findings

The proposed method is computationally efficient.

It generates diverse high-quality process models.

Outperforms or matches state-of-the-art algorithms.

Abstract

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions -- completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multi-objective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multi-objective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using Grey relational analysis combined with the Taguchi approach. {We have compared the proposed approach with the well-known single-objective algorithms and state-of-the-art multi-objective evolutionary algorithm -- Non-dominated Sorting Genetic Algorithm (NSGA-II).} Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state-of-the-art algorithms.