通过自适应-分布混合突变增强的蛇优化算法及其在能源储存系统容量优化中的应用
Yinggao Yue1, Li Cao1, Changzu Chen1
1School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.
Biomimetics (Basel, Switzerland)
|April 25, 2025
概括
这项研究引入了使用自适应t分布混合突变的增强蛇优化算法. 改进的方法实现了更快的融合和更高的准确性,优于传统技术.
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:
- 传统的蛇优化受到了随机初始化,缓慢的融合和低准确性的困扰.
- 解决这些局限性对于提高优化性能至关重要.
研究的目的:
- 提出一种适应性t分布混合突变蛇优化策略.
- 通过提高初始化,融合速度和精度来提高蛇优化算法的性能.
主要方法:
- 利用基于帐的混乱映射和准反向学习来实现人口初始化.
- 引入了适应性t分布混合突变食策略,以加强探索.
- 用一个异性吸引力机制取代交配模式,以改善全球搜索.
主要成果:
- 增强的蛇优化算法证明了加速的融合和改进的解决方案准确性.
- 与标准的蛇优化技术相比,提出的方法显示出更高的稳定性和准确性.
- 集成的改进协同增强了算法的整体性能.
结论:
- 适应性t分布混合突变蛇优化策略有效地克服了传统方法的缺点.
- 改进的算法实现了当地和全球开发能力之间的更好的平衡.
- 这种改进的优化技术为复杂的问题提供了更强大,更准确的解决方案.
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