鱼优化算法用于解决现实世界的优化问题.
Fu Yan1, Jin Zhang2, Jianqiang Yang2
1Guizhou Big Data Academy, Guizhou University, Guiyang, 550025, China. fyan3@gzu.edu.cn.
Scientific reports
|December 31, 2024
概括
本研究介绍了新的鱼优化算法 (COA),灵感来自鱼狩猎策略. 与复杂的优化任务的现有算法相比,COA在准确性,稳定性和速度方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 灵感来自自然的算法.
- 团结情报团队的人群.
背景情况:
- 灵感来自自然的算法为复杂的计算问题提供灵活和简单的解决方案.
- 仿真自然行为的生物算法是计算智能研究的一个关键领域.
- 鱼的狩猎策略为优化提供了一个独特的模型.
研究的目的:
- 介绍一款以自然为灵感的新算法,即鱼优化算法 (COA).
- 模拟鱼狩猎行为,特别是预谋和等待,进行优化.
- 评估COA在解决优化问题的现有算法方面的有效性.
主要方法:
- 根据观察到的鱼狩猎策略开发了鱼优化算法 (COA).
- 在29个标准数学测试函数上测试了COA.
- 将COA应用于5个现实世界的工程优化问题.
主要成果:
- 与类似的变体和最先进的算法相比,COA表现出更高的性能.
- 该算法在解决方案准确性,稳定性和融合速度方面显示出优势.
- 统计测试证实了COA的可行性和潜在应用.
结论:
- 鱼优化算法 (COA) 是一种高效的新型自然灵感优化技术.
- COA的表现验证了模仿鱼狩猎行为对计算智能的有效性.
- 该算法在解决具有挑战性的现实世界的优化问题方面显示出显著的前景.
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