多策略改进的鱼优化算法及其工程应用
1School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
改进的鱼优化算法 (ImWOA) 提高了全球搜索和融合速度. 这种新的算法解决了复杂的优化任务的原始鱼优化算法 (WOA) 的局限性.
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:
- 鱼优化算法 (WOA) 提供了简单性和良好的局部最佳避开,但受到低效的全球搜索和缓慢的融合的影响.
- 现有的WOA变种需要改进,以解决解决解决复杂优化问题的这些固有的局限性.
研究的目的:
- 引入一个改进的鱼优化算法 (ImWOA),克服标准WOA的全球搜索和融合速度限制.
- 通过新的战略,增强WOA的整体搜索能力和人口多样性.
主要方法:
- 实施了动态弹性边界优化策略,以在允许的范围内引导解决方案.
- 集成了一个先进的随机搜索策略,使用最佳和平均位置平衡全球和本地勘探.
- 采用组合突变机制来增加人口多样性并防止过早的融合.
主要成果:
- 在CEC2017基准函数上,ImWOA在5个元启发算法和3个WOA变体上表现出卓越的性能.
- 在各种维度场景 (30D和100D) 中实现了更高的优化准确性,稳定性和更快的融合速度.
- 成功地将ImWOA应用于现实世界的工程问题,包括减速器设计,车辆侧冲击和接梁设计.
结论:
- 拟议的ImWOA有效地解决了标准WOA的缺陷,提供了改进的全球搜索和融合.
- ImWOA显示出在多样化和复杂的工程优化领域应用的巨大潜力.
- 算法的稳定性和效率通过广泛的基准测试和现实世界的案例研究来验证.
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