修改了爬行动物搜索算法,用于全球优化问题的多狩猎协调策略
Di Wu1, Changsheng Wen2, Honghua Rao2
1School of Education and Music, Sanming University, Sanming 365004, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
这项研究通过多狩猎策略和学习技术来增强爬行动物搜索算法 (RSA),提高其解决复杂优化问题的能力并避免局部优化.
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:
- 原始的爬行动物搜索算法 (RSA) 在复杂问题中面临着融合和局部最佳的挑战.
- 它的狩猎和包围阶段可以导致后期代的停滞.
研究的目的:
- 提高全球搜索能力和地方最佳避免RSA.
- 开发一个修改的爬行动物搜索算法 (MRSA),以提高复杂优化任务的性能.
主要方法:
- 引入了整合拉格朗日位和基于教学优化 (TLBO) 学生阶段的多狩猎协调策略.
- 集成的镜头基于位置的学习 (LOBL) 和重新启动策略来增强本地最佳逃生.
主要成果:
- 修改后的爬行动物搜索算法 (MRSA) 在23个基准函数和CEC2020测试函数上表现出卓越的性能.
- 与最初的RSA相比,MRSA在全球能力和融合方面显示出显著的改进.
结论:
- 拟议的MRSA有效地解决了原始RSA的局限性,特别是在复杂的优化场景中.
- 在解决工程问题方面,MRSA具有很强的潜力,其应用在六个不同的工程任务中得到了验证.
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