通过多策略融合改进了 Osprey 优化算法
Wenli Lei1, Jinping Han1, Xinghao Wu1
1College of Physics and Electronic Information, Yan'an University, Yan'an 716000, China.
Biomimetics (Basel, Switzerland)
|November 26, 2024
概括
改进的奥斯普雷优化算法 (IOOA) 通过整合混乱映射,适应因素和变化策略来提高元启发性能. 这种新的方法提高了人口的多样性,并避免了局部最佳值,以获得更高的优化准确性.
科学领域:
- 计算智能是一种计算智能.
- 超听觉优化优化 超听觉优化
- 算法设计 算法设计
背景情况:
- Osprey优化算法 (OOA) 是一个有效的元启发,但受到了不平衡的探索/开发和过早的融合.
- 现有的OOA局限性包括对局部最佳的易受性和人口多样性的减少,阻碍了复杂的优化任务的性能.
研究的目的:
- 提出一个改进的 Osprey 优化算法 (IOOA),通过结合多种策略来克服原来的 OOA 的局限性.
- 提高全球勘探和当地开发平衡,增加人口多样性,提高融合速度和准确性.
主要方法:
- 使用Fuch混乱映射初始化,以增加最初的人口多样性.
- 在勘探阶段引入适应性加权因子,以提高趋同的准确性.
- 在开发阶段整合考西变异策略,以避免局部最佳状态并保持种群多样性.
- 从Sparrow搜索算法中整合一个警告机制,以平衡全球和本地搜索功能.
主要成果:
- 与其他优化算法相比,IOOA在10个基准测试函数和15个CEC2017测试函数中表现出卓越的性能.
- 非参数测试证实了IOOA的提高准确性和稳定性.
- 该IOOA成功地应用于三条架工程设计问题,展示了其在实际工程优化方面的有效性.
结论:
- 拟议的IOOA有效地解决了标准OOA的局限性,实现了增强的优化性能.
- 多策略融合方法显著提高了人口多样性,趋同准确性和逃避局部最佳状态的能力.
- IOOA显示出解决复杂的现实世界优化问题的巨大潜力,特别是在工程设计中.
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