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Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference.

Weiping Meng1,2, Yang He1, Yongquan Zhou1,2

  • 1College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.

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|January 24, 2025
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Summary
This summary is machine-generated.

This study introduces a Q-learning-driven butterfly optimization algorithm (QLBOA) to enhance optimization accuracy and diversity. The novel QLBOA effectively solves green vehicle routing problems, outperforming classical methods.

Keywords:
Q-learningbenchmark functionsbutterfly optimization algorithm (BOA)global optimizationgreen vehicle routing problem with time windows (GVRPTW)metaheuristic

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computational Optimization

Background:

  • Metaheuristic algorithms like Butterfly Optimization Algorithm (BOA) are widely used but can suffer from local optima and limited diversity.
  • Reinforcement learning offers mechanisms to improve decision-making and adaptability in optimization processes.

Purpose of the Study:

  • To develop an enhanced optimization algorithm by integrating Q-learning with BOA.
  • To improve optimization accuracy, prevent local optima, and increase population diversity.
  • To apply the proposed algorithm to the Green Vehicle Routing Problem (GVRP) with time windows and customer preferences.

Main Methods:

  • Integration of Q-learning mechanism into the Butterfly Optimization Algorithm (BOA) to create the Q-learning-driven butterfly optimization algorithm (QLBOA).
  • Introduction of Gaussian mutation with dynamic variance and migration mutation to enhance algorithm performance and population diversity.
  • Comparative analysis using 18 benchmark functions against five classical metaheuristic algorithms and three BOA variants.
  • Application of QLBOA to solve the GVRP with time windows, considering customer preferences, fuel consumption, carbon emissions, and costs.

Main Results:

  • QLBOA demonstrated superior performance compared to classical optimization algorithms on benchmark functions.
  • The algorithm effectively addressed the complexities of the green vehicle routing problem with time windows and customer preferences.
  • Analysis revealed the significant influence of decision-maker preferences and weight factors on various cost components.

Conclusions:

  • The proposed QLBOA offers a robust and effective approach for complex optimization tasks.
  • The integration of Q-learning and mutation strategies significantly enhances optimization capabilities.
  • QLBOA provides valuable insights for sustainable logistics and decision-making in green transportation.