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A Method for Growing Bio-memristors from Slime Mold
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泥模具算法的进步:一个全面的调查

Yuanfei Wei1,2, Zalinda Othman1, Kauthar Mohd Daud1

  • 1Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Biomimetics (Basel, Switzerland)
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

这篇评论详细介绍了粘液模具算法 (SMA),这是一个群集智能技术. 它涵盖了SMA理论,变体和优化和机器学习中的应用,并建议未来的研究方向.

关键词:
这种算法是Metaheuristic算法.优化的优化优化优化.粘液模具算法 (SMA) 是一个算法.群众情报是一个群众情报.

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科学领域:

  • 计算智能是一种计算智能.
  • 群集情报 群集情报 群集情报
  • 优化算法 优化算法

背景情况:

  • 粘液模具算法 (SMA) 是一种以自然为灵感的元启发.
  • 它模仿了粘液菌的食行为.
  • 由于其有效性,SMA已经获得了大量的研究兴趣.

研究的目的:

  • 提供关于粘液模具算法 (SMA) 的全面审查.
  • 总结SMA最近的进展和应用.
  • 确定SMA的未来研究机会.

主要方法:

  • 系统的文献审查来自谷歌学者 (130篇文章) (2022-2023年).
  • 描述SMA核心理论的描述.
  • 基于改善策略的SMA变体的分类.

主要成果:

  • 详细解释SMA的理论基础.
  • 各种改进的SMA变体的分类.
  • 概述SMA的应用领域,包括工程,能源,机器学习,网络,调度和图像细分.

结论:

  • 粘液模具算法是各种优化和人工智能任务中宝贵的工具.
  • 建议对SMA的多目标,离散和神经网络应用进行进一步的研究.
  • 审查强调了SMA在继续发展和更广泛应用方面的潜力.