一种新的探索阶段方法来改进鱼优化算法:解决现实世界工程设计问题的解决方案
1Electronics and Automation Department, Kırklareli University, 39010 Kırklareli, Turkey.
Biomimetics (Basel, Switzerland)
|June 25, 2025
概括
改进的鱼优化算法 (ICOA) 提高了勘探和开发平衡,克服了原来的鱼优化算法的局限性. 在复杂的优化任务和现实应用中,ICOA表现出卓越的性能.
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:
- 标准的鱼优化算法 (COA) 由于简化的竞争阶段缺乏随机性,因此在高维问题中遭受了早期收和糟糕的表现.
- 局限性包括无法有效平衡勘探和开采,导致局部最小值陷入困境并降低勘探能力.
- 夏季度假村阶段的低激活概率进一步阻碍了勘探,并减缓了趋同.
研究的目的:
- 提出一个改进的鱼优化算法 (ICOA),以解决原始COA的缺陷.
- 为了提高优化算法的勘探和开发能力之间的平衡.
- 提高高维度问题的性能,避免过早的趋同.
主要方法:
- 修改了COA的竞争阶段,其中有三个关键改进:一个自适应的步长机制,用于增加随机性的矢量映射,以及删除Xshade参数.
- 使用CEC-2014基准套件 (30个函数,10个和30个维度) 对最近的12个元启发式算法进行了ICOA的评估.
- 评估了五个工程设计问题和一个现实世界的广告支出回报率 (ROAS) 优化案例的性能.
主要成果:
- 在30个基准函数中,ICOA成功解决了24个基准函数,比COA和其他算法显著改进.
- 在工程应用中取得了最佳结果:在悬臂梁设计中的最佳重量 (1.339965公斤),在滚动元件轴承设计中的最大负载能力 (85,547.81N) 和ROAS优化中的最高性能 (144.601).
- 统计测试 (Wilcoxon Signed-Rank,t-test,弗里德曼等级) 证实了ICOA的优异表现,其平均排名为1.70,而COA在基准测试中的平均排名为4.13.
结论:
- 拟议的ICOA有效地增强了勘探和开采,克服了原来的COA的局限性.
- 在各种优化问题上,ICOA表现出强的性能,包括复杂的基准,工程设计和现实世界的场景.
- 这些修改显著提高了融合速度,解决方案的准确性,以及逃避局部最小值的能力,证明了它的有效性.
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