多重战略增强混合算法BAGWO结合甲虫天线搜索和灰狼优化器,用于全球优化
Fan Zhang1,2, Chuankai Liu3,4, Peng Liu1,2
1Research Institute of Aero-Engine, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China.
Scientific reports
|May 2, 2025
概括
一个新的混合优化算法,BAGWO,结合了虫天线搜索 (BAS) 和灰狼优化器 (GWO) 策略. 巴格沃在全球优化任务中表现出卓越的性能和稳定的趋同.
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:
- 像BAS和GWO这样的现有优化算法存在局限性.
- 混合化可以通过结合互补的优势来提高算法性能.
研究的目的:
- 介绍和评估BAGWO,一个新的混合优化算法集成BAS和GWO.
- 通过增强的机制,改进原来的BAS和GWO战略.
主要方法:
- 将甲虫天线搜索 (BAS) 和灰狼优化器 (GWO) 集成到BAGWO框架中.
- 引入了新的策略:基于西格体的魅力更新,基于等号的局部利用频率和自适应天线长度衰减.
- 通过对基准函数 (CEC 2005,CEC 2017) 和现实世界工程问题进行废除实验的验证.
主要成果:
- 巴格沃表现出稳定的收性质.
- 与现有算法相比,证明了优越的优化性能.
- 在广泛的测试中实现了更高的解决方案准确性和稳定性.
结论:
- 巴格沃有效地利用了巴斯和格沃的优势.
- 拟议的增强措施显著提高了优化能力.
- 巴格沃显示出强大的竞争力和实际全球优化应用的潜力.
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