一种混合非线性大鼠算法与正弦-正弦算法,用于全球优化和受限制的工程应用
Jinzhong Zhang1, Anqi Jin2, Tan Zhang1
1School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.
新的Sine-Cosine Greater Cane Rat算法 (SCGCRA) 通过提高解决方案的准确性和适应性来增强群体智能. 这个算法解决了基本GCRA的局限性,在复杂的优化任务中提供了卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 群体情报算法 群体情报算法
- 优化技术 优化技术
背景情况:
- 大鼠算法 (GCRA) 灵感来源于动物的食行为,但有参数灵敏度,准确性低,适应性差.
- 现有的群集智能算法经常在平衡探索和利用方面扎,导致过早的融合或停滞.
研究的目的:
- 引入一个混合的Sine-Cosine Greater Cane Rat算法 (SCGCRA),以克服基本GCRA的局限性.
- 为了提高搜索效率,准确性和适应性,解决复杂的优化问题.
- 为了平衡勘探和开发,以确定全球最佳解决方案.
主要方法:
- 一种混合方法,将Sine-Cosine算法 (SCA) 与大鼠算法 (GCRA) 结合起来.
- 从SCA中整合非线性控制策略和周期性振荡波动来调节搜索动态.
- 在23个基准函数和6个受约束的工程设计问题上测试SCGCRA.
主要成果:
- 与基本GCRA相比,SCGCRA在各种优化任务中表现出更高的性能.
- 实现了更快的融合速度,更高的解决方案精度和更好的稳定性.
- 展示了增强的人口多样性和适应能力,有效地避免了当地最佳状态和停滞.
结论:
- 拟议的SCGCRA有效地解决了基本GCRA的缺点,提供了更高的效率和准确性.
- 混合方法提供了协同效应,增强了算法的解决复杂工程和基准问题的能力.
- 对于需要强大,准确的解决方案的现实世界优化应用程序,SCGCRA具有显著的潜力.
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