一个改进的线性预测演化算法,基于拓的基于对立的学习,用于优化
A M Mohiuddin1, Jagdish Chand Bansal1
1South Asian University, New Delhi, India.
MethodsX
|December 27, 2023
概括
一种基于拓对立的新型学习策略增强了改进的线性预测演化算法 (ILPE). 这种方法将人口动态视为时间序列,提高了优化问题解决的有效性和准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式技术 超启发式技术
背景情况:
- 基于预测的进化算法是新兴的一类元启发式优化技术.
- 改进的线性预测演化算法 (ILPE) 是最近的一种元启发式,其灵感来源于非线性最小方位拟合模型.
研究的目的:
- 将基于对立的拓学习纳入ILPE框架.
- 在进化算法中开发一种新的繁殖运算符,用于生成试验个体.
主要方法:
- 拟议的算法,拓改进线性预测演化 (TILPE),利用非线性最小方形拟合模型与拓对立式学习相结合.
- TILPE将人口序列视为时间序列数据,以预测后续的人口代.
- 构建了一个新的复制运算符,取代了传统的突变和交叉运算符.
主要成果:
- 在CEC2014和CEC2017基准函数上的数值实验证明了算法的有效性.
- 提议的TILPE算法在解决复杂的优化问题方面表现出很高的效率.
结论:
- 基于拓对立的学习的整合显著增强了ILPE.
- TILPE提供了一种有前途的方法,通过时间序列预测和基于对立的学习来推进元启发式优化技术.
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