一个记住的多目标Sinh-Cosh优化器,用于解决多目标工程设计问题
Doaa El-Nagar1, Ibrahim Zeidan2, Mohamed Issa3,4
1Computer and Systems Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt. Doaaelnagar@zu.edu.eg.
Scientific reports
|January 21, 2026
概括
多目标Sinh-Cosh优化算法 (MOSCHO) 通过使用记住的局部最佳值来增强多目标优化. 这种新的方法改善了复杂的工程问题的融合和多样性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 工程应用 工程应用
背景情况:
- 多目标优化问题在各种科学和工程领域提出了重大挑战.
- 现有的优化算法经常在复杂的现实应用中与平衡融合和多样性作斗争.
研究的目的:
- 介绍和评估多目标Sinh-Cosh优化算法 (MOSCHO),这是Sinh-Cosh优化器的扩展.
- 通过将记住的本地最佳与全球解决方案集成,提高寻找非主导解决方案的能力.
- 评估MOSCHO在基准函数和现实世界工程设计问题上的有效性.
主要方法:
- 拟议的MOSCHO算法在Sinh-Cosh优化框架内结合了记住的局部最佳策略.
- 算法的搜索空间由整合本地和全球最佳解决方案来指导更新过程.
- 绩效评估涉及七个标准指标,将MOSCHO与已建立的多目标优化算法进行比较.
主要成果:
- 莫斯科在测试的功能和应用中展示了显著的融合和多样性能力.
- 该算法在ZDT3,ZDT4和MMF14基准函数上取得了卓越的性能.
- 莫斯科在SRN受约束函数和现实世界工程问题上表现强 (>75%的指标).
结论:
- 多目标Sinh-Cosh优化算法 (MOSCHO) 是解决复杂多目标优化任务的强大而有效的方法.
- 整合已记住的局部最佳值对MOSCHO寻找高质量的非主导解决方案的能力作出了重大贡献.
- 莫斯科由于其强大的性能和适应性,对现实世界的工程设计应用特别有希望.
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