一个多策略的优化算法及其应用
Yang Yang1, Maosheng Fu1, Xiancun Zhou1
1College of Electronic and Information Engineering, West Anhui University, Lu'an 237012, China.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
一个新的混乱-高斯-巴里中心优化 (CGBPO) 算法增强了种群多样性,并避免了局部最佳. 这种智能优化方法提高了复杂工程任务的融合速度和准确性.
科学领域:
- 计算智能是一种计算智能.
- 工程优化优化工程优化
- 超启发式算法 (Metaheuristic Algorithms) 是一种算法,可以通过
背景情况:
- 智能优化算法对于复杂的工程挑战至关重要.
- 标准的Parrot优化 (PO) 算法存在局部最佳陷和缓慢的融合问题.
- 需要进行改进,以提高PO的性能和适用性.
研究的目的:
- 介绍一个新的增强的优化算法,混沌-高斯-巴里中心优化 (CGBPO).
- 为了解决标准PO算法的局限性,特别是过早的融合和局部最佳.
- 评估CGBPO在基准函数和实际工程问题上的表现.
主要方法:
- 纳入混乱的物流映射,以增强最初的人口多样性.
- 斯突变的应用,以防止过早的汇聚到局部最佳状态.
- 集成 barycenter 基于对立的学习,在代过程中扩大搜索空间.
主要成果:
- 在CEC2017和CEC2022基准套件中,CGBPO表现优于其他七种算法.
- 拟议的算法实现了更快的融合,更高的解决方案准确性和更好的稳定性.
- 在室内可见光定位模拟中,CGBPO提供了比标准PO算法更准确的位置估计.
结论:
- 混沌高斯-巴里中心优化 (CGBPO) 算法有效地克服了标准PO的局限性.
- 在优化任务的融合速度,准确性和稳定性方面,CGBPO显示出显著的改进.
- 改进的算法在实际工程应用中表现出卓越的适应性和稳定性,包括可见光定位.
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