一个改进的虫搜索算法,用于解决优化问题
1School of Electrical Engineering, Northeast Electric Power University, 169 Changchun Road, Jilin 132012, China.
Biomimetics (Basel, Switzerland)
|February 26, 2026
概括
改进的灵长搜索算法 (IMSA) 通过自适应地控制搜索阶段并改善人口多样性来增强优化. 这种新的方法实现了比传统方法更快的融合和更高的准确性.
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:
- 传统的灵搜索算法 (MSA) 面临着缓慢的融合和局部最佳的挑战.
- 复杂的优化问题需要更强大,更适应的算法.
研究的目的:
- 引入一个改进的灵长搜索算法 (IMSA),克服传统MSA的局限性.
- 在复杂的优化场景中增强融合速度,准确性和稳定性.
主要方法:
- 开发了一种适应性概率转换因子,以便在勘探和开发阶段之间顺利过渡.
- 实施了子空间和全空间概率更新策略,以调整动态范围.
- 引入了使用欧几里德距离和健身标准的精英人口选机制.
- 设计了一个基向量适应性概率选择机制,以提高相关性.
- 在性食人主义中利用有针对性的随机干扰和非贪的替代,以保持人口多样性.
主要成果:
- IMSA在趋同速度,计算精度和稳定性方面展示了显著的优势.
- 对29个CEC2017测试函数的实验结果显示,与原始MSA和其他五个领先的元启发算法相比,其性能优越.
结论:
- 拟议的IMSA有效地解决了传统MSA的局限性.
- 对于复杂的优化问题,IMSA提供了更强大,更有效的解决方案,其性能优于现有的最先进的算法.
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