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April 23, 2026International Journal of Mathematical Modelling and Numerical Optimisation

Optimising capacitated EV routing with quantum evolutionary algorithms and federated reinforcement learning

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Authors

KSK. SarangamDMDharani Kumar Chowdary MirappalliDRDr.K. Raghava Rao

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Overview

Hybrid framework optimises route and charging strategies in EV logistics, indicating improved efficiency.

Key Points

  • The research aims to enhance routing and charging strategies for capacitated electric vehicles using advanced algorithms.
  • Developed a quantum-inspired hybrid evolutionary framework integrating quantum-inspired evolutionary algorithms and federated reinforcement learning.
  • Modeled CEVRP as a mixed-integer nonlinear program with stochastic energy consumption and multiple objectives.
  • Evaluated the framework using large-scale datasets to measure performance against existing methods.
  • Achieved a 7.6% reduction in travel distance for electric vehicles.
  • Demonstrated a 21.3% increase in computational efficiency in routing decisions.
  • Showed improved convergence compared to traditional methods like DPCA, CBACO, and SIGALNS.

Cite This Study

Sarangam et al. (2026) studied this question.

synapsesocial.com/papers/69e9b89b85696592c86ebc58https://doi.org/10.1504/ijmmno.2026.153027
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