一个基于数值优化问题的最佳-最差管理的增强的模算法
Tongzheng Li1, Hongchi Meng2, Dong Wang3
1Salford Business School, University of Salford, Manchester M5 4WT, UK.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
新的BWSMA算法通过整合适应性贪,最佳-最差管理和停滞的替代机制来增强群体智能. 这种改进的Slime Mould算法 (SMA) 变种在优化任务中表现出卓越的性能和稳定性.
科学领域:
- 群体情报
- 计算智能
- 优化算法
背景情况:
- 模算法 (SMA) 是一种流行的群集智能化方法.
- 现有的SMA版本面临局限性,包括缓慢的融合和局部最佳.
- "没有免费午餐"定理强调了对算法的专业化和改进的需要.
研究的目的:
- 提出一种名为BWSMA的模算法 (SMA) 的新变体.
- 提高SMA的汇聚速度,人口质量和逃离当地最佳的能力.
- 通过全面的实验验证BWSMA的有效性和稳定性.
主要方法:
- 在SMA中整合了三种新机制:适应性贪,最佳-最差管理和停滞的替代.
- 使用CEC2018和CEC2022基准测试套件进行广泛的实验验证.
- 与三种衍生算法,八种SMA变体以及其他八种改进算法进行比较分析.
- 使用威尔科克森等级和弗里德曼和内梅尼测试进行统计分析.
- 应用于两个结构优化问题,以评估现实世界的适用性.
主要成果:
- 在各种测试套件中,BWSMA显著超过了所有比较的算法.
- 与SMA变体和其他改进的算法相比,BWSMA的平均排名更高.
- 统计测试证实了BWSMA的显著性能优势.
- 该算法在解决结构优化问题方面表现出强大的适用性.
结论:
- 拟议的BWSMA是一个高效和强大的优化算法.
- 综合机制成功地解决了原来的SMA的缺陷.
- 它提供了卓越的搜索准确性,
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