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Updated: Jul 7, 2025

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MLBRSA:基于多学习的爬行动物搜索算法用于全球优化和软件需求优先级问题

Jeyaganesh Kumar Kailasam1, Rajkumar Nalliah2, Saravanakumar Nallagoundanpalayam Muthusamy3

  • 1Department of Artificial Intelligence and Data Science, M. Kumarasamy College of Engineering, Karur 639113, Tamilnadu, India.

Biomimetics (Basel, Switzerland)
|December 22, 2023
PubMed
概括

本研究介绍了基于多学习的爬行动物搜索算法 (MLBRSA),它结合了Q学习,竞争性和适应性学习. MLBRSA有效地解决复杂的工程问题,并优先考虑软件要求.

关键词:
这就是Q-learning.适应性学习是一种适应性学习.竞争性学习 竞争性学习基于多学习的爬行动物搜索算法 (MLBRSA)优化的优化优化优化.软件需求的优先级设置 软件需求的优先级设置

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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