混合多策略混沌转折食黑猩猩优化算法研究研究
Xiaorui Yang1,2, Yumei Zhang3,1,2, Xiaojiao Lv1,2
1School of Computer Science, Shaanxi Normal University, Xi'an, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
一个新的混沌跳捕食黑猩猩优化算法 (CSFChOA) 提高了融合速度和准确性. 这种改进的算法在全球优化和实际工程设计问题上表现出色.
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:
- 黑猩猩优化算法 (ChOA) 患有缓慢的融合和对局部优化的敏感性.
- 现有的智能优化算法往往难以保持多样性并达到高精度.
- 解决这些局限性对于有效解决复杂计算任务中的问题至关重要.
研究的目的:
- 提出一个混沌跳捕食黑猩猩优化算法 (CSFChOA),以克服 ChOA 的局限性.
- 为了提高收速度,提高准确性,并防止过早收到局部最佳.
- 在标准测试函数和工程设计问题上验证算法的性能.
主要方法:
- 介绍一种猫混沌序列,用于生成多样化的初始解决方案.
- 应用基于对立的学习来选择优秀的初始群体.
- 实施一个转食策略,使用最佳解决方案作为一个枢纽,以增加人口多样性和搜索范围.
主要成果:
- 在23个标准和CEC2019测试函数上,CSFChOA表现出比CHOA和其他算法更好的性能.
- 使用威尔科克森等级总和测试进行的统计分析证实了CSFChOA的稳定性和趋同准确性.
- 在工程设计问题上观察到显著的改进,包括减速器的成本降低了100%,三条杆架的成本降低了6.77%.
结论:
- 拟议的CSFChOA有效地解决了原来的CHOA的融合速度和准确性问题.
- 混沌序列的整合,基于对立的学习和跳跃的食增强了全球优化能力.
- 在解决复杂的工程设计问题方面,CSFChOA表现出强大的可行性,适用性和优越性.
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