发展鱼优化算法:MISWOA的开发和分析
Chunfang Li1,2, Yuqi Yao1, Mingyi Jiang3
1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了多群改进的螺旋优化算法 (MISWOA),以提高全球搜索和融合速度. 在优化任务中,MISWOA显著优于传统的鱼优化算法 (WOA) 和其变体.
科学领域:
- 计算智能是一种计算智能.
- 群体情报算法 群体情报算法
- 超听觉优化优化 超听觉优化
背景情况:
- 传统的鱼优化算法 (WOA) 在全球搜索能力和融合速度方面面临局限性.
- 现有的WOA变种往往难以有效地平衡勘探和开采.
- 需要强大的优化算法,适用于复杂的工程问题.
研究的目的:
- 为了引入一个增强的鱼优化算法,多群改进的螺旋鱼优化算法 (MISWOA).
- 解决传统的WOA在全球搜索和融合速度方面的缺陷.
- 为了提高整体效率,稳定性和优化过程的精度.
主要方法:
- 整合一个自适应的非线性收因子.
- 整合了可变增益补偿机制和自适应权重.
- 实施先进的螺旋趋同战略和多种人口机制.
主要成果:
- MISWOA展示了显著增强的全球搜索能力和融合速度.
- 与WOA及其变体相比,该算法显示出更高的精度和稳定性.
- 通过模拟和实验进行广泛的验证证实了MISWOA的有效性.
结论:
- 与传统的WOA及其衍生品相比,MISWOA是一个显著的改进.
- 拟议的算法提供了卓越的性能和工程应用的实际潜力.
- 这项工作为推进群体智能算法研究提供了一个系统的框架.
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