改进了aquila优化器,用于对复杂工程问题的集群解决方案
Himanshu Sharma1, Krishan Arora1, Raghav Mahajan1
1School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, India.
Scientific reports
|December 27, 2024
概括
一个改进的Aquila优化器 (IAO) 通过模仿狩猎行为来增强传统方法. 这种新的元启发算法在解决复杂的优化问题和工程应用中表现出卓越的性能.
科学领域:
- 人工智能的人工智能
- 计算优化计算优化
- 超启发式算法 超启发式算法 超启发式算法
背景情况:
- 传统的优化方法经常面临诸如局部最佳,缓慢的融合和在未知的空间中低效的搜索等挑战.
- 现有的单一解决方案方法限制了复杂问题解决的有效性和生产力.
研究的目的:
- 介绍一个改进的Aquila优化器 (IAO),一个新的元启发式算法,灵感来自Aquila的狩猎策略.
- 与现有方法相比,提高优化能力,效率和生产力.
主要方法:
- 国际天文组织的算法模拟了Aquila的狩猎过程,包含了不同的阶段:低空飞行与悠的下降 (开发),高海拔潜水和轮飞行 (探索),以及俯冲机动 (捕获).
- 通过使用23个经典优化函数和5个现实世界的工程问题来评估IAO的性能.
- 对比分析包括收曲线,时间复杂性和威尔科克森等级总和测试.
主要成果:
- 在经典优化函数上,IAO表现出了与各种冠军算法相比的卓越性能.
- 该算法在应用于现实世界的工程挑战时,在各种应用领域中被证明是有效的.
- 时间复杂性分析显示,最佳时间为0.00015225,优于其他算法,威尔科克森等级总和测试的p值<0.05,表明统计学意义.
结论:
- 改进的Aquila优化器 (IAO) 是一个有弹性和适应性的工具,用于解决具有挑战性的优化问题.
- IAO表现出显著的效率和竞争力,将其定位为现实世界工程应用的宝贵优化工具.
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