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一个新的以人工为灵感的优化算法,用于贸易中心的位置和分配方法

Shuhan Hu1, Gang Hu1, Bo Du2

  • 1Department of Applied Mathematics, Xi'an University of Technology, Xi'an 710054, China.

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概括
此摘要是机器生成的。

一个新的人工优化算法 (AEOA) 有效地解决了贸易中心的位置和分配问题,降低了建设和运输成本,以改善物流.

关键词:
人工智能优化算法低成本的运输贸易中心的位置

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科学领域:

  • 运营研究
  • 计算智能
  • 优化算法

背景情况:

  • 城市之间的高效货物转移对于经济活动至关重要.
  • 现有的贸易中心定位和分配方法可能不具有成本效益或最佳性.
  • 减少建设和运输成本是物流网络设计的一个关键目标.

研究的目的:

  • 提出一个新的贸易中心定位和分配方法.
  • 开发一个人工优化算法 (AEOA) 来解决这个复杂的问题.
  • 减少与货物转移相关的总体成本.

主要方法:

  • 制定了一个贸易中心的位置和分配模型,其目标是建设和运输成本.
  • 开发了一种基于迁移行为的新型人工优化算法 (AEOA).
  • 将情境意识,自由探索和飞行策略纳入AEOA.

主要成果:

  • 与其他八个算法相比,AEOA在12个基准函数中的11个表现出色.
  • 定量分析证实了AEOA的更快的趋同速度和更强的稳定性.
  • 模拟的案例研究表明该方法能够选择最佳的枢纽并降低成本.

结论:

  • 通过AEOA推出的贸易中心位置和分配方法有效地降低了物流成本.
  • 对于复杂的优化问题,AEOA是一种强大而高效的算法.
  • 这种方法为政府在物流规划中的决策提供了宝贵的工具,河南省的案例研究证明了这一点.