一种混合增强的差异进化,用于优化优化
Mojtaba Ghasemi1, Mohsen Zare2, Pavel Trojovský3
1Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了狩猎差分进化 (HDE),这是一个优化算法,结合了差分进化 (DE) 和灰狼优化器 (GWO). HDE显著提高了对优化问题的融合速度和解决方案质量.
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:
- 差异进化 (DE) 是一种广泛使用的优化算法.
- 德的关键局限性是其缓慢的趋同率.
- 现有的DE变种经常在过早的融合和局部最佳状态下扎.
研究的目的:
- 为了提高微分进化算法的融合率和解决准确性.
- 通过将灰狼优化器 (GWO) 与DE集成,引入一种新的混合优化算法.
- 评估拟议的狩猎差异进化 (HDE) 算法的性能与已建立的基准.
主要方法:
- 提出了一个新的混合算法,狩猎差异进化 (HDE).
- HDE将DE的搜索能力与GWO的融合加速相结合.
- 交叉速率和突变概率的参数调整允许平衡DE和GWO强度.
主要成果:
- 在CEC-2019和CEC-2014基准函数上,HDE算法表现出卓越的性能.
- 像HDE/current-to-rand/1和HDE/current-to-best/1这样的特定变体比其他HDE变体和现有算法显著改善.
- 在17个测试的功能上,jHDE变体显示出了比jDE显著的改进.
结论:
- 提出的HDE算法有效地加速了融合,并改善了最佳解决方案的发现.
- HDE克服了标准DE算法的局限性,包括避免局部最小值.
- 对于复杂的优化任务,HDE提供了一种强大而高效的方法.
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