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优化器:对医学问题的算法和应用.

Junbo Lian1, Guohua Hui1, Ling Ma1

  • 1College of Mathematics and Computer Sciences, Zhejiang A & F University, Hangzhou, 311300, PR China; Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Zhejiang A & F University, Hangzhou, 311300, PR China; Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, 311300, PR China.

Computers in biology and medicine
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概括
此摘要是机器生成的。

一个新的优化器 (PO) 通过模仿的行为,有效地解决复杂的问题. 这种新的随机优化方法在工程和医疗应用的勘探和开发方面显示了竞争优势.

关键词:
遗传算法 遗传算法 遗传算法医疗问题 医疗问题这是一种元启发式 (metaheuristic) 启发.优化优化 优化优化优化器的优化器群体优化群体的优化

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科学领域:

  • 计算智能是一种计算智能.
  • 超启发式优化优化方法
  • 生物启发的算法生物启发的算法.

背景情况:

  • 随机优化对于复杂的研究挑战至关重要.
  • 现有的方法需要加强勘探和开发能力.

研究的目的:

  • 介绍优化器 (PO),一个新的生物灵感算法.
  • 评估PO的表现与各种基准函数的既定算法对比.
  • 评估PO在工程和医学领域的适用性.

主要方法:

  • 根据Pyrrhura Molinae的行为开发了优化器 (PO).
  • 基于35个功能的基准PO,包括IEEE CEC 2022测试套件.
  • 进行参数灵敏度分析,并将PO应用于工程和医学问题.

主要成果:

  • PO表现出具有竞争力的探索和开发特征.
  • 在基准测试中,PO超过了几种流行的算法.
  • 在工程设计,疾病诊断和医学图像细分方面,PO被证明是有效和优越的.

结论:

  • 优化器 (PO) 是一个有前途且具有竞争力的随机优化算法.
  • 在各种应用中,PO比现有方法具有显著的优势.
  • 开源代码和补充材料可用于PO算法.