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Optimal Foraging00:48

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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通过多策略融合改进了 Osprey 优化算法.

Wenli Lei1, Jinping Han1, Xinghao Wu1

  • 1College of Physics and Electronic Information, Yan'an University, Yan'an 716000, China.

Biomimetics (Basel, Switzerland)
|November 26, 2024
PubMed
概括

改进的奥斯普雷优化算法 (IOOA) 通过整合混乱映射,适应因素和变化策略来提高元启发性能. 这种新的方法提高了人口的多样性,并避免了局部最佳值,以获得更高的优化准确性.

科学领域:

  • 计算智能是一种计算智能.
  • 超听觉优化优化 超听觉优化
  • 算法设计 算法设计

背景情况:

  • Osprey优化算法 (OOA) 是一个有效的元启发,但受到了不平衡的探索/开发和过早的融合.
  • 现有的OOA局限性包括对局部最佳的易受性和人口多样性的减少,阻碍了复杂的优化任务的性能.

研究的目的:

  • 提出一个改进的 Osprey 优化算法 (IOOA),通过结合多种策略来克服原来的 OOA 的局限性.
  • 提高全球勘探和当地开发平衡,增加人口多样性,提高融合速度和准确性.

主要方法:

  • 使用Fuch混乱映射初始化,以增加最初的人口多样性.
  • 在勘探阶段引入适应性加权因子,以提高趋同的准确性.
  • 在开发阶段整合考西变异策略,以避免局部最佳状态并保持种群多样性.
  • 从Sparrow搜索算法中整合一个警告机制,以平衡全球和本地搜索功能.

主要成果:

  • 与其他优化算法相比,IOOA在10个基准测试函数和15个CEC2017测试函数中表现出卓越的性能.
  • 非参数测试证实了IOOA的提高准确性和稳定性.
  • 该IOOA成功地应用于三条架工程设计问题,展示了其在实际工程优化方面的有效性.
关键词:
考契的变化是不同的.太多的混乱地图绘制.适应性权衡因素是适应性的权衡因素.奥斯普雷优化算法的优化算法

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结论:

  • 拟议的IOOA有效地解决了标准OOA的局限性,实现了增强的优化性能.
  • 多策略融合方法显著提高了人口多样性,趋同准确性和逃避局部最佳状态的能力.
  • IOOA显示出解决复杂的现实世界优化问题的巨大潜力,特别是在工程设计中.