原子搜索优化:对其变体,应用和未来方向的全面审查
Mohammed A El-Shorbagy1, Anas Bouaouda2, Laith Abualigah3,4
1Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
PeerJ. Computer science
|March 10, 2025
概括
这篇评论综合了五年的原子搜索优化 (ASO) 研究,详细介绍了其变体和应用. 它突出了ASO的存在.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 分子动力学模拟模型
背景情况:
- 原子搜索优化 (ASO) 是一个在2019年开发的元启发算法,灵感来自分子动力学.
- 它使用列纳德-斯和键长潜能模拟原子相互作用.
- 尽管它取得了成功,但需要对ASO实施进行全面审查.
研究的目的:
- 提供五年原子搜索优化 (ASO) 进展的全面审查.
- 综合关键的ASO变体,它们的原则和成就.
- 为未来的ASO研究和实践指导提供分类学.
主要方法:
- 从2019年到现在的ASO研究的系统文献综述.
- 分析ASO变种,包括修改和杂交.
- 跨多个科学和技术领域的ASO应用程序的分类.
主要成果:
- 已经开发了几种ASO变体,用于单个和多个目标的优化.
- ASO已成功应用于工程,医疗,物联网,数据挖掘,环境建模和安全领域.
- 工程是ASO最突出的应用领域.
结论:
- ASO是一种多功能优化算法,具有多种成功实现.
- 该研究提供了对实际ASO应用的见解,并为开发定制变体提供了指导.
- 本次审查解决了对ASO成功和未来研究方向进行全面评估的需要.
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