多目标二进制微分方法与参数调整用于发现业务流程模型:国防部-ProM
A Sonia Deshmukh1, B Shikha Gupta2, C Naveen Kumar3
1Department of Computer Science and Information Technology KIET Group of Institutions, Ghaziabad, Uttar Pradesh, India.
TheScientificWorldJournal
|September 4, 2024
概括
这项研究引入了过程发现的多目标框架,提高了模型质量. 二元差异进化方法产生多样化,高质量的过程模型,优于现有方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 业务流程管理 业务流程管理
背景情况:
- 过程发现算法自动从业务数据中生成过程模型.
- 传统的方法往往只产生一个模型,冒着不准确和过度拟合的风险.
- 模型质量是使用诸如完整性,精确性,简单性和概括性等维度来评估的.
研究的目的:
- 解决单一模型过程发现的局限性.
- 开发一个多目标框架,用于生成多种候选过程模型.
- 让用户根据特定的背景需求选择模型.
主要方法:
- 制定了过程发现作为一个多目标优化问题.
- 采用二元差异演化与二分法交叉/突变运算符.
- 使用灰色关系分析和塔古奇方法调整参数.
- 与单一目标算法和NSGA-II.II.相比.
主要成果:
- 拟议的二进制微分进化方法在计算上是高效的.
- 它产生多样化的候选解决方案,具有高健身评分.
- 生产的模型优于或与最先进的算法相当.
结论:
- 多目标过程发现提供了更灵活,更准确的方法.
- 二元差异进化为生成高质量,多样化的过程模型提供了一种有效的方法.
- 该方法通过提供多个合适的模型选项来增强用户选择.
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