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一个改进的黑翼风算法与工程优化的先进策略优化工程
Sarada Mohapatra1, Deepa Kaliyaperumal2, Farhad Soleimanian Gharehchopogh3
1Department of EEE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India. msmohapatra.sarada@gmail.com.
Scientific reports
|May 21, 2025
概括
改进的黑翼飞算法 (RBKA) 通过使用后勤地图和混乱行为来增强优化. 这种新的方法提高了融合速度和为复杂问题寻找全球解决方案.
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:
- 经典的优化算法经常面临着与收速度和局部最佳停滞的挑战.
- 黑翼算法 (BKA) 是一种元启发式优化技术,可用于复杂的问题解决.
研究的目的:
- 引入改进的黑翼风算法 (RBKA),这是BKA的增强版本.
- 提高全球搜索效率,融合速度,以及BKA.Local Optimum逃脱能力.
主要方法:
- 使用后勤地图进行人口初始化,用于增强全球搜索.
- 一种新的搜索策略,结合混乱扰动和布朗运动来实现勘探-开发平衡.
- 基于对立的学习 (OBL) 来解决局部最佳停滞,并改善全球解决方案的识别.
主要成果:
- 在基准函数 (CEC2005,CEC2020,CEC2022) 上,RBKA表现出卓越的表现.
- 该算法在约束优化问题 (CEC2011) 和复杂的工程设计任务中被证明是有效的.
- 对比分析显示,RBKA在融合速度,稳定性和整体有效性方面表现优于其他情报算法.
结论:
- RBKA是一个强大的和可适应的优化算法.
- 提议的改进大大提高了原始BKA的性能.
- 对于解决复杂的优化挑战,RBKA提供了一个有前途的解决方案.
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