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Fuzzy Particle Swarm Optimization Algorithm (NFPSO) for Reachability Analysis of Complex Software Systems

EasyChair Preprint no. 4328

11 pagesDate: October 8, 2020


Nowadays, model checking is applied as an accurate technique to verify software systems. The main problem of model checking techniques is the state space explosion. This problem occurs due to the exponential memory usage by the model checker. In this situation, using meta-heuristic and evolutionary algorithms to search for a state in which a property is satisfied/violated is a promising solution. Recently, different evolutionary algorithms like GA, PSO, etc. are applied to find deadlock state. Even though useful, most of them are concentrated on finding deadlock. This paper proposes a fuzzy algorithm in order to analyze reachability properties in systems specified through GTS with enormous state space. To do so, we first extend the existing PSO algorithm (for checking deadlocks) to analyze reachability properties. Then, to increase the accuracy, we employ a Fuzzy adaptive PSO algorithm to determine which state and path should be explored in each step to find the corresponding reachable state. These two approaches are implemented in an open-source toolset for designing and model checking GTS called GROOVE. Moreover, the experimental results indicate that the hybrid fuzzy approach improves speed and accuracy in comparison with other techniques based on meta-heuristic algorithms such as GA and the hybrid of PSO-GSA in analyzing reachability properties.

Keyphrases: Fuzzy Adaptive Particle Swarm Optimization, Graph Transformation System, model checking, reachability property, state space explosion

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Nahid Salimi and Vahid Rafe and Hamed Tabrizchi and Amir Mosavi},
  title = {Fuzzy Particle Swarm Optimization Algorithm (NFPSO) for Reachability Analysis of Complex Software Systems},
  howpublished = {EasyChair Preprint no. 4328},

  year = {EasyChair, 2020}}
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