MOEA for discovering Pareto-optimal process models: an experimental comparison

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 multiobjective 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 multiobjective 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 multiobjective evolutionary algorithm—Nondominated 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.

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Solving a Business Process Optimization Issue With a Genetic Algorithm Coupled With Multi-Criteria Decision Analysis Method

Mahammed

Benslimane

2021

International Journal of Organizational and Collective Intelligence

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The addressed issue in the present work revolves around the area of business process management in general and in particular optimization. The problem involves the generation of optimized business process designs from a business process model in a multi-criteria optimization environment by appealing an evolutionary algorithm. Thus, the main contribution is to analyze the characteristics of using a multi-criteria decision-analysis method within a genetic algorithm in an issue of business process optimization. The experimental results clearly demonstrated that using a multi-criteria decision-analysis method helps considerably the production of qualitatively interesting alternative solutions in a reasonable period time regard-ing the problem complexity, which ultimately assists the decision maker to perform improved decision making

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MOEA for discovering Pareto-optimal process models: an experimental comparison

Cited by 3 publications

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Genetic Algorithm Based on Multiple Population in a Business Process Optimization Issue

Genetic Algorithm Based on Multiple Population in a Business Process Optimization Issue

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

Solving a Business Process Optimization Issue With a Genetic Algorithm Coupled With Multi-Criteria Decision Analysis Method

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