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.

翻译：流程发现方法通过分析业务数据自动揭示结构化信息，即流程模型。流程模型的质量通过多个质量维度进行衡量——完整性（重放拟合度）、精确性、简洁性和泛化性。传统的流程发现算法通常仅输出单一流程模型，而单一模型可能无法准确捕捉观测行为并容易对训练数据产生过拟合。我们将流程发现问题构建在多目标优化框架中，从而为终端用户提供多个候选解决方案，用户可根据本地环境约束（可能动态变化）选择合适的模型。本研究采用多目标框架下的二进制差分进化方法解决流程发现任务。所提出的方法采用二分交叉/变异算子，并利用灰色关联分析结合田口方法进行参数调调优。{我们将所提方法与著名的单目标算法以及先进的多目标进化算法——非支配排序遗传算法（NSGA-II）进行了比较。}此外，还通过计算质量维度加权平均值进行了补充比较。结果表明，所提算法计算效率高，能生成多样化的候选解，且在适应度函数上得分优异。研究证明，所提方法生成的流程模型优于或至少等同于当前最先进算法生成的模型。