基于混合策略的多门图像细分 改进的Dingo优化算法
Qianqian Zhu1, Min Gong1, Yijie Wang1
1College of Design, Hanyang University, Ansan 15588, Gyeonggi-do, Republic of Korea.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
一个新的混合策略改进的丁戈优化算法 (HSIDOA) 通过改进本地开发和全球勘探来提高优化性能. 这种先进的算法在复杂的数值任务和图像分割中显示出优异的结果,显示出强大的融合和稳定性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 图像处理 图像处理
背景情况:
- 像Dingo优化算法 (DOA) 这样的标准优化算法经常在复杂问题中与局部最佳,缓慢的融合和边界处理扎.
- 现有的方法需要加强全球勘探和精确的当地开发机制,以提高效率.
研究的目的:
- 引入混合策略改进的丁戈优化算法 (HSIDOA),以克服标准DOA的局限性.
- 在复杂的优化任务中增强融合准确性,速度和稳定性.
主要方法:
- HSIDOA集成了二次插值搜索,横向交叉搜索和以中心点为基础的对立学习来处理边界.
- 该算法在CEC2017 (30-D) 和CEC2022 (10/20-D) 基准套件上对七种主流算法进行了测试.
- HSIDOA被应用于多级值图像分割,使用Otsu的方法作为目标函数.
主要成果:
- 在基准套房中,HSIDOA在平均适应性,标准偏差,收率和弗里德曼排名方面表现出卓越的表现.
- 该算法在各种维度中表现出强大的稳定性和可扩展性.
- 在图像细分方面,HSIDOA在多个值级别 (4-10) 中始终达到最佳质量,在合速度和稳定性方面超过了比较算法.
结论:
- HSIDOA在全球勘探,当地开发,融合速度和高维稳定性方面提供了显著的改进.
- 该算法是一种高效,稳定和多功能工具,用于复杂的数值优化和图像细分.
- HSIDOA为解决各种领域具有挑战性的优化问题提供了一个强大的框架.
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