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The minimum regret path problem on stochastic fuzzy time-varying networks

Wei Huang1, Zhilei Xu1, Liehuang Zhu1

  • 1School of Cyberspace Science and Technology, Beijing Institute of Technology, 100081 Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 11, 2022
PubMed

Insights

We introduce a stochastic fuzzy time-varying minimum regret path problem and propose a novel random fuzzy delay neural network (RFDNN) to solve it efficiently. The RFDNN demonstrates superior performance in finding minimum regret paths in complex networks.

Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Network Science

Background:

  • Stochastic fuzzy time-varying networks present challenges for pathfinding due to uncertainty and dynamic edge weights.
  • Existing shortest path algorithms may not adequately address the 'minimum regret' criterion in such complex environments.

Purpose of the Study:

  • To introduce and define the stochastic fuzzy time-varying minimum regret path problem (SFTMRP).
  • To develop a novel, training-free neural network approach for solving the SFTMRP.

Main Methods:

  • The proposed Random Fuzzy Delay Neural Network (RFDNN) utilizes specialized neurons with six distinct layers.
  • Information exchange within the RFDNN involves shortest path and maximum probability solution signals.
  • Theoretical analysis covers time-complexity and correctness, validated by numerical examples.

Main Results:

  • The RFDNN effectively addresses the SFTMRP by finding paths with minimal regret.
  • Experimental results on stochastic fuzzy time-varying road networks show significant performance improvements over existing methods.
  • The algorithm's efficiency was tested on networks with 1000-5000 nodes.

Conclusions:

  • The RFDNN is a viable and efficient method for solving the SFTMRP.
  • This approach offers a significant advancement in finding optimal paths in uncertain, dynamic network conditions.
Keywords:
Random fuzzy delay neural networkStochastic fuzzy time-varying minimum regret pathStochastic fuzzy time-varying networkStochastic fuzzy time-varying shortest path

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