使用灰狼Aquila协同算法用于结构工程中的设计问题
Megha Varshney1, Pravesh Kumar1, Musrrat Ali2
1Rajkiya Engineering College, Dr. APJ Abdul Kalam Kalam Technical University, Bijnor 246725, India.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
这项研究通过整合灰狼优化器 (GWO) 策略和准对立式学习 (QOBL) 来增强Aquila Optimizer (AO). 混合方法改善了复杂的优化问题的探索和噪声稳定性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 阿奎拉优化器 (AO) 是有效的,但可以有有限的探索.
- 现有的元启发算法可能会与杂的客观函数作斗争.
研究的目的:
- 为了提高阿奎拉优化器 (AO) 的勘探能力和噪声强度.
- 通过将灰狼优化器 (GWO) 和准对立式学习 (QOBL) 与AO集成来开发混合优化算法.
主要方法:
- 一种混合方法,将AO与GWO的alpha位置相结合,用于搜索指导.
- 在AO算法的每个阶段应用准对立式学习 (QOBL).
- 在23个标准测试函数和CEC2017测试函数上对拟议的混合算法进行基准测试.
主要成果:
- 混合AO-GWO-QOBL算法与其他元启发算法相比,表现出优异的性能.
- 改进的算法在基准和工程问题上表现出卓越的有效性.
- 整合GWO提高了AO对杂目标功能的稳定性.
结论:
- 拟议的混合优化技术有效地解决了AO算法的探索局限性.
- 整合GWO和QOBL显著提高了Aquila优化器的性能和稳定性.
- 增强的算法适用于解决具有不确定的搜索空间的复杂工程问题.
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