一个混合的非线性鱼优化算法与 sinus cosine 进行全球优化
1School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
混合的Sine Cosine算法 (SCA) 和鱼优化算法 (WOA),称为SCWOA,提高了优化性能. 这种新的方法提高了复杂问题的精度和融合速度.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 鱼优化算法 (WOA) 面临的局限性包括精度有限和过早的融合.
- 无边共边算法 (SCA) 在人口多样性和搜索区域放大方面提供了改进.
研究的目的:
- 开发一个与SCA (SCWOA) 集成的混合非线性WOA.
- 为了解决标准WOA的缺陷,并增强优化能力.
- 评估SCWOA在基准功能和工程设计方面的表现.
主要方法:
- 鱼优化算法 (WOA) 与正弦共弦算法 (SCA) 的混合.
- 开发SCWOA以改善全球和本地搜索.
- 与其他优化算法 (BA,CapSA,MFO,MVO,SAO,MDWA,WOA) 的比较分析.
主要成果:
- 与现有的算法相比,SCWOA显示出更高的融合效率和计算利能力.
- 混合方法成功地整合了勘探和开采,避免了过早的融合.
- SCWOA实现了更高的计算精度和更快的融合速度.
结论:
- SCWOA算法为优化任务提供了强大的和实用的解决方案.
- 这种混合方法有效地克服了个别算法的局限性.
- 斯科瓦显示了对现实世界工程设计问题的巨大潜力.
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