一个增强的基于反对派的非洲优化器,用于解决工程设计问题和全球优化
Henry Blankson1, Vanisree Chandran1, Himadri Lala2
1Department of Mathematics, SAS, VIT University, Vellore, Tamil Nadu, 632014, India.
Scientific reports
|September 26, 2025
概括
本研究介绍了基于增强的反对派的非洲优化器 (EOBAVO),通过加速收和逃避局部最佳值来改善优化. 对于复杂的优化挑战,EOBAVO提供了一种高效的解决方案.
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:
- 传统的非洲优化 (AVO) 在粗略的搜索空间中面临限制,需要过度的代.
- 逃避局部最佳值和加速融合是元启发优化中的关键挑战.
研究的目的:
- 通过使用改进的基于对立的学习技术来增强非洲优化算法.
- 开发一种新的优化算法,即基于增强的反对派的非洲优化器 (EOBAVO),以提高性能.
主要方法:
- 加强基于对立的学习 (EOBL) 与非洲优化 (AVO) 算法的集成.
- 使用CEC2005和CEC2022基准函数和七个工程问题进行实验验证.
- 使用t测试和Wilcoxon等级总和测试进行统计分析来比较性能.
主要成果:
- 与现有的领先优化算法相比,提出的EOBAVO表现出优越的性能.
- 欧巴沃有效地加速了融合,并帮助逃脱了当地最佳状态.
- 统计测试证实了EOBAVO.com的显著优异表现.
结论:
- 基于增强的反对派的非洲优化器 (EOBAVO) 是一款用于复杂优化任务的有能力和高效的算法.
- 集成EOBL显著提高了AVO算法的能力.
- 欧巴沃在计算智能和优化领域提出了一个有前途的进步.
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