Minimum Initial Marking Estimation in Labeled Petri Nets through Genetic Algorithm and Tabu Search
Marwa Gam, Amir Abdellatif, Achraf Jabeur Telmoudi, Lotfi Nabli · 2020
This paper develops estimation algorithms to compute minimal initial markings, following the observation of sequences of labels produced by a labeled Petri net. We focus on the running time. For that purpose, we investigate meta-heuristic based method and demonstrate that such methods have the potential to solve efficiently the problem. Genetic algorithm is suitable to compute a large number of random sequences. Then, the Tabu search algorithm is used to find the minimum initial marking of the different random sequences in a labeled Petri net (LPN).