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According to valence bond theory, a covalent bond results when: (1) an orbital on one atom overlaps an orbital on a second atom, and (2) the single electrons in each orbital combine to form an electron pair. The strength of a covalent bond depends on the extent of overlap of the orbitals involved. Maximum overlap is possible when the orbitals overlap on a direct line between the two nuclei.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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基于多策略的解决工程优化问题 粒子群优化 混合花优化算法 优化算法

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  • 1School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

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概括
此摘要是机器生成的。

新的粒子群集优化混合 dandelion优化 (PSODO) 算法增强了群集智能方法. 它提高了优化速度,并避免了局部极端问题,以便更好地解决问题.

关键词:
这是一次重型飞机的飞行.公英算法 公英算法功能优化 优化 功能优化多目标优化多目标优化粒子群优化算法 粒子群优化算法

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科学领域:

  • 计算智能是一种计算智能.
  • 群体情报优化 群体情报优化

背景情况:

  • 群体智能方法广泛用于机械设计,微电网调度,无人机技术,神经网络训练和多目标优化.
  • 公英优化算法面临着缓慢的优化速度和对局部极端的易受影响的挑战.

研究的目的:

  • 提出一种混合算法,粒子群优化混合花优化 (PSODO),解决标准花优化算法的局限性.
  • 增强优化算法的多样性和搜索能力.

主要方法:

  • 一种混合方法,将粒子群优化 (PSO) 与公英优化算法相结合.
  • 结合PSO的全球搜索功能和公英算法的独特的个人更新规则 (上升,下降,降落).
  • 通过公英的上升和下降阶段平衡全球和本地搜索.

主要成果:

  • 该PSODO算法显示显著改善了全球最佳价值搜索能力.
  • 与其他算法相比,增强了融合速度和整体优化速度.
  • 对22个基准函数和3个工程设计问题 (CEC 2005) 进行了有效性验证.

结论:

  • 该PSODO算法有效地克服了原始 dandelion优化算法的局限性.
  • 混合方法在全球勘探和当地开发之间提供了卓越的平衡.
  • 对于复杂的问题,PSODO提出了一个可行的和有效的优化技术.