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Parameter Optimization Strategy of Fuzzy Petri Net Utilizing Hybrid GA-SFLA Algorithm

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Simulation Tools and Techniques (SIMUtools 2019)

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Abstract

Fuzzy Petri net (FPN) is a powerful tool to model and analyze the knowledge-based systems (KBSs) or expert systems (ESs). The accuracy of the reasoning result is a bottleneck to hinder the further development of FPN because of lacking self-learning capability. To overcome this issue, a hybrid GA-SFLA algorithm is proposed in this paper to improve the precision of each parameter of a given FPN model. The proposed algorithm combines the advantages both of GA and SFLA and includes three phases, which are generating chromosome by encoding the multi-dimensional solution which reflects all initial frogs, gaining a better individual as well as seeking the optimal solution by executing the local search and global search operations of SFLA. Finally, an FPN model is used to test the feasibility of the proposed algorithm. Simulation results reveal that all parameters of the given FPN model have the higher precision by implementing the GA-SFLA than that of implementing GA and SFLA, respectively.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (Nos. 61741205, 61462029).

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Authors and Affiliations

  1. College of Information Science and Engineering, Jishou University, Jishou, 416000, Hunan, China

    Wei Jiang, Kai-Qing Zhou & Li-Ping Mo

Authors
  1. Wei Jiang
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  2. Kai-Qing Zhou
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  3. Li-Ping Mo
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Corresponding author

Correspondence to Kai-Qing Zhou .

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Editors and Affiliations

  1. Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics Aeronautics, UESTC, Chengdu, China

    Dingde Jiang

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jiang, W., Zhou, KQ., Mo, LP. (2019). Parameter Optimization Strategy of Fuzzy Petri Net Utilizing Hybrid GA-SFLA Algorithm. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_40

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  • DOI: https://doi.org/10.1007/978-3-030-32216-8_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32215-1

  • Online ISBN: 978-3-030-32216-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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