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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Q学习驱动的蝶优化算法用于绿色车辆路由问题 考虑客户偏好

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

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

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概括
此摘要是机器生成的。

本研究介绍了一种Q学习驱动的蝶优化算法 (QLBOA),以提高优化准确性和多样性. 新的QLBOA有效地解决了绿色车辆路线问题,优于经典方法.

关键词:
这就是Q-learning.基准值的功能是基准值的功能.蝶优化算法 (BOA) 是一个全球优化全球优化绿色车辆路由问题与时间窗口 (GVRPTW)这是一种元启发式 (metaheuristic) 听证.

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科学领域:

  • 人工智能的人工智能
  • 运营研究 运营研究
  • 计算优化计算优化

背景情况:

  • 像蝶优化算法 (BOA) 这样的元启发算法被广泛使用,但可能会遭受局部最佳和有限的多样性.
  • 强化学习提供了改善决策和优化过程中的适应能力的机制.

研究的目的:

  • 通过将Q-learning与BOA集成来开发一个增强的优化算法.
  • 为了提高优化准确度,防止局部优化,增加人口多样性.
  • 将拟议的算法应用于绿色车辆路由问题 (GVRP),以时间窗口和客户偏好.

主要方法:

  • 将Q学习机制集成到蝶优化算法 (BOA) 中,以创建Q学习驱动的蝶优化算法 (QLBOA).
  • 引入高斯突变与动态变异和迁移突变,以提高算法性能和人口多样性.
  • 使用18个基准函数进行比较分析,与5个经典的元启发算法和3个BOA变体进行比较.
  • 应用 QLBOA 解决 GVRP 时间窗口,考虑客户偏好,燃料消耗,碳排放和成本.

主要成果:

  • 与基准函数的经典优化算法相比,QLBOA在基准函数上表现出更高的性能.
  • 该算法有效地解决了绿色车辆路由问题的复杂性,与时间窗口和客户偏好有关.
  • 分析显示,决策者偏好和权重因素对各种成本组件的影响很大.

结论:

  • 拟议的QLBOA为复杂的优化任务提供了强大而有效的方法.
  • 整合Q学习和突变策略显著提高了优化能力.
  • QLBOA为可持续物流和绿色运输的决策提供了宝贵的见解.