改进了基于全球优化和工程问题的双色互补机制的 Osprey 优化算法
Fengtao Wei1, Xin Shi1, Yue Feng1
1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.
Biomimetics (Basel, Switzerland)
|August 28, 2024
概括
改进的 Osprey 优化算法 (IOOA) 通过引入一种新的两种颜色互补机制来提高全球优化准确性. 这种方法有效地避免了局部优化,提高了复杂的优化任务的性能.
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:
- Osprey 优化算法 (OOA) 的优化准确性有限,并倾向于汇聚到局部最佳值.
- 有效的全球优化对于解决复杂的工程和科学问题至关重要.
研究的目的:
- 提出一个改进的 Osprey 优化算法 (IOOA),增强全球搜索能力和优化准确性.
- 解决OOA标准的局限性,特别是其对局部最佳的敏感性.
主要方法:
- 引入"双色互补机制",以增强人口多样性和搜索能力.
- 集成后勤混沌映射,良好的点设定方法,四色理论,哈里斯启发式,漫步和漫步,优化螺旋搜索和火虫扰动策略.
- 使用基准函数和CEC2020/CEC2022测试集的验证,使用ANOVA,Wilcoxon和弗里德曼测试.
主要成果:
- 与现有方法相比,IOOA显著提高了与收的准确性和速度.
- 拟议的两种颜色互补机制有效地提高了全球搜索能力和避免局部最小值.
- 统计测试证实了IOOA的显著业绩改善.
结论:
- 在全球优化问题上,IOOA在准确性和速度方面提供了卓越的性能.
- 新的两种颜色互补机制是算法的增强有效性的关键因素.
- IOOA显示了工程优化应用的高实用价值和优势.
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