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Updated: Sep 15, 2025

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一个增强的季节优化算法用于数值优化和工程设计.
Hojjat Emami1, Mojtaba Fardi2, Babak Azarnavid3
1Department of Computer Engineering, University of Bonab, Bonab, Iran. emami@ubonab.ac.ir.
Scientific reports
|July 15, 2025
概括
增强季节优化 (ESO) 算法改进了标准季节优化 (SO) 算法,通过结合新的运算符来提高优化任务中的搜索性能和解决方案质量.
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:
- 标准季节优化 (SO) 显示出有希望的结果,但在勘探-开发平衡方面存在困难,导致过早的趋同.
- 现有的优化算法在实现高质量解决方案和高效融合方面经常面临挑战.
- 在复杂的数值和工程优化问题上,自然启发的算法越来越重要.
研究的目的:
- 引入增强季节优化 (ESO) 算法,解决标准SO的局限性.
- 在优化任务中提高搜索性能,解决方案质量和融合速度.
- 为了评估ESO的有效性与广泛的既定和新的优化算法.
主要方法:
- 欧洲社会组织整合了四个新型运营商:在当地开发的根蔓延,人口多样性的野火,质量和趋同的精细竞争/抵抗,以及以反对为基础的学习,以避免局部最佳.
- 对比分析涉及25个数值优化函数和4个工程设计问题.
- 性能与基础 (PSO,DE),高性能 (CMAES,LSHES,RRTO,ALA,THRO) 和其他受自然启发的算法 (SO,HO,CBKA) 相比进行了基准测试.
主要成果:
- 在测试的基准指标中,ESO显著超过了标准SO算法.
- 与所有同行优化器相比,ESO在解决方案质量和融合方面表现出竞争性或优异的表现.
- 在16/25数值函数和3/4工程问题上,ESO排名第一,在数字基准的弗里德曼测试中获得最佳平均排名.
结论:
- 增强季节优化 (ESO) 算法有效地克服了标准SO的局限性,提供了更好的搜索性能和解决方案质量.
- ESO表现出强的表现,特别是在移动,复合和高维 (1000-D) 数值问题以及可扩展性分析方面.
- ESO在元启发优化方面取得了重大进展,为复杂的计算问题提供了强大的工具.
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