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Updated: Jul 15, 2025

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混合粘液模具和算术优化算法与随机中心学习和重启突变
Hongmin Chen1, Zhuo Wang1, Heming Jia1
1Department of Information Engineering, Sanming University, Sanming 365004, China.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
本研究介绍了一种混合优化算法,结合了粘液模具算法 (SMA) 和算术优化算法 (AOA),提高了复杂函数的性能. 这种新的方法提高了对汇率的准确性和全球搜索效率,以实现元启发式优化.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 模算法 (SMA) 在全球搜索中表现出色,但在后期的振荡中扎.
- 算术优化算法 (AOA) 通过乘法/除法运算符提供强大的随机性和收性.
- 现有的算法在复杂的功能优化和找到最佳位置方面面临着挑战.
研究的目的:
- 开发一个混合优化算法,整合SMA和AOA.
- 提高全球搜索效率,人口多样性和趋同准确性.
- 在复杂的优化任务中解决单个算法的局限性.
主要方法:
- 整合SMA和AOA,取代SMA与AOA运营商的融合阶段.
- 整合一个随机的中央解决方案战略,以改善全球搜索和多样性.
- 实施重新启动和突变策略,以提高趋同准确度和后期优化.
主要成果:
- 混合算法 (RCLSMAOA) 在对各种测试函数的比较实验中显示出有效性.
- 统计测试 (威尔科克森等级和,弗里德曼) 证实了业绩的提高.
- 与单个方法相比,增强的算法显示出更高的收精度和优化能力.
结论:
- 建议的混合粘液模具和算术优化算法 (RCLSMAOA) 对复杂的优化问题是有效的.
- 随机中央学习,突变和重启策略的整合显著提高了性能.
- 该算法显示了在实际工程问题中应用的前景.
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