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Updated: Jun 13, 2025

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对 Gazelle Optimizer及其变体进行比较分析
Raghav Mahajan1, Himanshu Sharma1, Krishan Arora1
1School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, India.
Heliyon
|September 16, 2024
概括
这项研究增强了加优化算法 (GOA) 的适应性,强度飞行,轮盘和随机步行策略. 改进的GOA变种在优化任务和工程问题上表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 灵感来自大自然的算法
背景情况:
- 盖塞尔优化算法 (GOA) 是一种以自然为灵感的元启发算法,有效用于复杂的优化.
- 现有的政府机关在各种场景的效率和稳定性方面存在局限性.
- 需要进行改进,以扩大其适用性和性能.
研究的目的:
- 为了引入加塞尔优化算法 (GOA) 的新变体.
- 通过战略性改进,提高澳大利亚政府的效率和稳定性.
- 在基准和现实世界问题上评估增强的GOA变体的性能.
主要方法:
- 将适应性战略融入阿联政府.
- 纳入利维的飞行战略,以加强勘探.
- 轮盘选择和随机走路策略的应用.
- 对CEC 2014,CEC 2017,工程问题和THD最小化进行严格的测试.
主要成果:
- 拟议的GOA变种表现出比原来的GOA更优异的性能.
- 改进的算法显示提高了效率和稳定性.
- 在解决工程和总波扭曲 (THD) 最小化问题的成功应用.
结论:
- 新的GOA变体在优化能力方面提供了显著的改进.
- 综合战略有效地解决了原来的阿联政府的局限性.
- 增强的算法为复杂的优化挑战提供了更有效的工具.
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