一个混合群智能优化算法用于天线设计问题.
Supreet Singh1, Harbinder Singh2, Nitin Mittal3
1School of Computer Science, UPES, Dehradun, Uttarakhand, India.
Scientific reports
|February 5, 2025
概括
一个新的基于Salp Swarm和海优化的裸鼠算法 (SSNMRA) 提高了优化性能. 这种混合方法提高了对汇率的准确性和复杂电磁问题的探索.
科学领域:
- 计算智能是一种计算智能.
- 优化算法的优化算法
- 电磁学 电磁学 电磁学 电磁学
背景情况:
- 元启发式算法对于复杂的问题解决至关重要,但经常面临局部最佳等局限性.
- 裸鼠算法 (NMRA) 显示出潜力,但需要在趋同和准确性方面进行改进.
- 现有的算法面临着各种优化挑战,需要新的方法.
研究的目的:
- 引入一个改进的裸鼠算法 (NMRA),命名为Salp Swarm和基于海优化的NMRA (SSNMRA).
- 改善原始NMRA的勘探能力和融合性能.
- 为了验证SSNMRA在基准测试套件和电磁优化任务上的有效性.
主要方法:
- 裸体鼠算法 (NMRA) 与Salp Swarm算法 (SSA) 和海优化算法 (SOA) 的混合.
- 整合SSA和SOA搜索机制,以提高NMRA的勘探和开发平衡.
- 使用CEC 2019基准测试套件的验证和实际的电磁优化问题.
主要成果:
- 与现有的最先进的算法相比,SSNMRA表现出更高的性能.
- 拟议的混合算法实现了增强的融合精度.
- 观察到整体优化能力的显著改善.
结论:
- 对于复杂的优化问题,特别是电磁领域,SSNMRA提供了一个有前途的解决方案.
- SSA和SOA的整合有效地解决了NMRA的局限性.
- 该算法显示了先进应用的潜力,例如复杂的天线设计.
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