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Multiobjective Binary Differential Approach with Parameter Tuning for Discovering Business Process Models: MoD-ProM.

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This study introduces a multiobjective framework for process discovery, enhancing model quality. The Binary Differential Evolution approach generates diverse, high-quality process models, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Business Process Management

Background:

  • Process discovery algorithms automatically generate process models from business data.
  • Traditional methods often produce a single model, risking inaccuracy and overfitting.
  • Model quality is assessed using dimensions like completeness, preciseness, simplicity, and generalization.

Purpose of the Study:

  • To address limitations of single-model process discovery.
  • To develop a multiobjective framework for generating diverse candidate process models.
  • To enable users to select models based on specific contextual needs.

Main Methods:

  • Formulated process discovery as a multiobjective optimization problem.
  • Employed Binary Differential Evolution with dichotomous crossover/mutation operators.
  • Tuned parameters using grey relational analysis and the Taguchi approach.
  • Compared against single-objective algorithms and NSGA-II.

Main Results:

  • The proposed Binary Differential Evolution approach is computationally efficient.
  • It generates diversified candidate solutions with high fitness scores.
  • Models produced are superior or comparable to state-of-the-art algorithms.

Conclusions:

  • Multiobjective process discovery offers a more flexible and accurate approach.
  • Binary Differential Evolution provides an effective method for generating high-quality, diverse process models.
  • The approach enhances user choice by providing multiple suitable model options.