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相关概念视频

Hybrid Zones02:29

Hybrid Zones

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Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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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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GP4ESP:一种混合基因算法和粒子群优化算法,用于边缘服务器放置.

Fang Han1, Hui Fu1, Bo Wang2

  • 1Huanghe Science and Technology University, Zhengzhou, China.

PeerJ. Computer science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了GP4ESP,这是一个用于边缘服务器放置 (ESP) 的混合元启发算法. 通过结合遗传算法和粒子群优化,GP4ESP显著减少了整体响应时间,以实现高效的边缘计算.

关键词:
边缘计算是一种边缘计算.边缘部署 边缘部署遗传算法 遗传算法 遗传算法粒子群集优化优化 粒子群集优化服务器的位置 服务器的位置

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科学领域:

  • 计算机科学 计算机科学
  • 分布式系统 分布式系统
  • 人工智能的人工智能

背景情况:

  • 边缘计算为智能设备和智能应用程序提供了至关重要的超低延迟服务.
  • 边缘服务器配置 (ESP) 对于高效的请求处理至关重要,但是一个NP难题.
  • 现有的ESP元启发算法由于单一策略的利用或忽视计算延迟而存在局限性.

研究的目的:

  • 制定ESP问题,目的是尽量减少整体响应时间,考虑异质边缘服务器.
  • 提出一个有效的混合元启发算法来解决ESP问题.
  • 评估拟议的算法的性能与现有方法相比.

主要方法:

  • 制定了边缘服务器配置 (ESP) 问题,考虑异质边缘服务器并最大限度地减少整体响应时间.
  • 开发了一个混合的元启发算法,GP4ESP,整合了遗传算法 (GA) 和粒子集群优化 (PSO).
  • 进行了广泛的模拟实验,以评估GP4ESP的性能.

主要成果:

  • 与11个最先进的ESP算法相比,GP4ESP实现了18.2%-20.7%的总体响应时间缩短.
  • 在ESP问题的不同尺度上,GP4ESP的性能改进是一致的.
  • 混合方法有效地将GA的全球搜索与PSO的快速融合融合在一起.

结论:

  • 对于边缘服务器放置问题,GP4ESP提供了一个优越的解决方案,其性能优于现有的算法.
  • 混合元启发式方法有效地解决了单一策略算法和计算延迟考虑的局限性.
  • 这项工作有助于更高效,更有效的边缘计算资源管理.