计算机技术与发展2024，Vol.34Issue(7)：154-160,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0114
基于遗传算法的最小初始标识估计
Minimal Initial Marking Estimation Based on Genetic Algorithm
摘要
Abstract
When considering label sequences,computing the minimal initial marking set of a labeled Petri net with unobservable transitions is a complex task.Existing methods to address this issue come with various limitations.We employ a genetic algorithm-based approach to estimate the minimal initial markings.Due to the potential existence of multiple initial markings(often infinite),the focus lies in obtaining a minimal initial marking set in the Petri net,satisfying the following conditions:the initial markings allow at least one firing se-quence consistent with the observed label sequences and network structure;the initial markings have the minimum total token count(i.e.,the lowest sum of tokens over all places),and for each observed label,allowance is made for at most one unobservable transition firing before each observable transition firing.Given that estimating the minimal initial markings falls within the NP-hard category,the a-doption of such algorithms is reasonable.We demonstrate the effectiveness of the proposed method through practical experiments.Compared with the existing algorithm,the proposed algorithm can obtain the minimum initial marking estimate with a lower computational cost.关键词
离散事件系统/Petri网/初始标识估计/不可观测变迁/变迁触发序列Key words
discrete event systems/Petri nets/initial marking estimates/unobservable transition/transition firing sequence分类
信息技术与安全科学引用本文复制引用
卞宏亚..基于遗传算法的最小初始标识估计[J].计算机技术与发展,2024,34(7):154-160,7.基金项目
山东省自然科学基金资助项目(ZR2020MF094) （ZR2020MF094）
国家自然科学基金资助项目(61402216) （61402216）
