基于干部群体与导师关系的教学优化算法,用于解决复杂的优化问题的机制
Xiao Wu1, Shaobo Li2, Fengbin Wu2
1School of Mechanical Engineering, Guizhou University, Guiyang 550025, China.
一个新的教学学习优化算法 (TLOCTO) 改进了原来的TLBO,通过解决缓慢的融合和局部优化. 这种增强的算法在基准测试和工程应用中显示出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 基于教学学习的优化 (TLBO) 算法在实际问题上很受欢迎,但有其局限性.
- 现有的TLBO算法在缓慢的融合,局部最佳和次优性能方面扎.
研究的目的:
- 介绍一种新的算法,即基于与导师机制 (TLOCTO) 的干部群体关系的教学优化算法.
- 通过结合班级干部设置和课外学习机构特征来增强TLBO算法.
主要方法:
- 开发了一个新的学习者战略,干部群众关系战略和导师机制.
- 在23个测试函数和CEC-2020基准函数上评估了TLOCTO算法.
- 使用Wilcoxon签名的排名和总和测试,将TLOCTO与其他流行的优化器进行了比较.
主要成果:
- TLOCTO算法证明了更好的融合速度,解决方案准确性和稳定性.
- 实验结果证实了增强算法的强有力的竞争力.
- 统计测试验证了TLOCTO对其他优化器的优越性.
结论:
- 提议的TLOCTO算法有效地克服了原来的TLBO的局限性.
- TLOCTO显示了解决复杂优化问题的巨大潜力.
- 该算法的实际应用性通过成功应用到工程设计问题得到证实.
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