基于机器学习和信息概念的启发式算法的改进策略:对海马优化算法的审查
1School of Economics, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China.
PeerJ. Computer science
|June 26, 2025
概括
这项研究介绍了一种新的基于K-最近邻居 (KNN) 的惯性重量优化策略,用于像海马优化算法 (SHO) 这样的算法. 这种动态方法通过将重量与距离联系起来来提高优化,而不仅仅是代,提高性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化方法
- 机器学习应用程序 机器学习应用程序
背景情况:
- 传统的惯性重量优化方法在metaheuristics具有机械限制.
- 现有的方法通常依赖于基于固定的代的惯性权重调整.
- 需要更具适应性和动态性的惯性重量策略.
研究的目的:
- 提出一种新的惯性重量优化策略,灵感来自K-最近邻居 (KNN),以提高元启发性能.
- 引入基于的机制,以改善交叉和突变期间的信息继承.
- 将这些策略集成到海马优化算法 (SHO) 中,并评估它们的有效性.
主要方法:
- 开发了一种基于KNN原理的动态惯性重量调整策略,将距离与重量映射出来.
- 将重分配机制纳入交叉和突变运算符中.
- 将建议的策略集成到海马优化算法 (SHO) 中.
- 使用CEC2005和CEC2021测试套件中的31个基准函数验证了改进的SHO.
主要成果:
- 改进后的SHO算法在融合速度,解决方案精度和稳定性方面取得了显著的改进.
- 废弃实验证实,物流-KNN惯性重量策略和基于的交叉突变机制各自有助于提高性能.
- 连续的"距离-重量"映射证明比离散的"代-重量"映射更具适应性.
结论:
- 拟议的物流-KNN惯性重量优化和基于的交叉突变机制有效地增强了海马优化算法.
- 基于距离的动态惯性重量调整提供了卓越的适应性和优化能力.
- 这项研究为改善复杂优化问题的元启发算法性能提供了有希望的方法.
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