矩阵学习粒子群集优化用于多目标多代理采集和随时间交付 Windows
IEEE transactions on cybernetics
|November 26, 2025
概括
本研究介绍了一种强大的优化方法,用于多代理采集和交付安排. 新的矩阵学习粒子群集优化 (MLPSO) 算法提高了复杂物流任务的解决方案质量和多样性.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 物流管理物流管理
背景情况:
- 多代理系统被广泛用于接送和交付任务,需要解决方案,满足客户的时间窗口,尽管潜在的干扰.
- 目前使用多个模拟的评估方法耗时且间接.
- 需要明确有效的方法来评估调度解决方案的稳定性.
研究的目的:
- 为代理安排问题制定一个强大的优化目标,明确评估到达时间与时间窗口的到达时间.
- 将问题建模为一个三目标优化,考虑稳定性,产量和成本.
- 提出一种先进的优化算法,用于生成多样化和高质量的解决方案.
主要方法:
- 定义了基于代理到达时间和客户时间窗口进行明确评估的稳定性优化目标.
- 制定了这个问题作为一个三目标优化问题,结合了强度,产能和成本.
- 拟议的矩阵学习粒子群集优化 (MLPSO) 具有基于矩阵的解决方案表示 (邻近和分配矩阵).
- 开发了一种基于矩阵的远程学习 (MDL) 策略,用于粒子更新和双空间本地搜索 (DSLS),以增强融合和多样性.
主要成果:
- MLPSO有效地获得多样化和高质量的解决方案,用于收集和交付安排.
- 基于矩阵的表示和MDL策略有助于提取最佳任务细分和代理分配.
- DSLS进一步提高了获得的解决方案的融合和多样性.
- 实验结果表明,MLPSO在各种规模的解决方案质量和多样性方面优于最先进的算法.
结论:
- 拟议的稳定性优化目标为调度解决方案提供了一个明确有效的评估方法.
- MLPSO是一种强大而有效的算法,用于解决复杂的,多目标的采集和交付问题.
- 该方法显著提升了强大的物流调度和多代理任务优化方面的最新技术.
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