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基于花优化器的混沌逆泥模具算法的实现
Yi Zhang1, Yang Liu1, Yue Zhao1
1College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130119, China.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
本研究介绍了一种增强的混合优化算法,结合了粘液模具算法 (SMA) 和 dandelion优化器功能. 改进的方法提高了对比速度和精度,用于复杂的问题,如功率负载预测.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 模算法 (SMA) 和花优化器是元启发式算法.
- 现有的算法可能会遭受缓慢的融合和局部最佳陷.
- 对于像极端学习机器 (ELM) 这样的机器学习模型的参数优化对于性能至关重要.
研究的目的:
- 开发一种新的混合优化算法,提高了融合速度和全球搜索功能.
- 通过结合混乱映射,布朗运动,莱维飞行和镜像反射学习来增强粘液模具算法的性能.
- 在功率负载预测中应用改进的算法来优化极端学习机器 (ELM) 参数.
主要方法:
- 模算法 (SMA) 的混合化与混合的花优化器.
- 对人口多样性的伯努利混乱映射的整合.
- 结合布朗运动和莱维飞行以进行增强的全球和本地搜索.
- 镜像反射学习的应用,以避免在以后的代中局部最佳.
主要成果:
- 与现有方法相比,拟议的混合算法在标准测试函数上表现出更高的融合速度和精度.
- 实验验证显示了优化性能的显著改善.
- 优化的极端学习机器 (ELM) 模型实现了有效的功率负载预测.
结论:
- 开发的混合算法有效地解决了传统优化技术的局限性.
- 增强的SMA变体为复杂的优化任务提供了强大的性能.
- 该方法在实际工程应用中被证明是有效的,特别是在功率负载预测中.
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