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.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
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.
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.
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