多层感知子分组和稀疏高斯式基于过程的替代器辅助进化算法,用于昂贵的多目标优化
IEEE transactions on cybernetics
|November 27, 2025
概括
本研究介绍了MLPSGP-SAEA,这是一种新的算法,将多层感知子分组与稀疏的高斯过程相结合,以高效地解决昂贵的多目标优化问题. 它提高了复杂的优化任务的计算效率和准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法的优化算法
- 机器学习是机器学习.
背景情况:
- 由于不确定性量化,高斯过程 (GPs) 对昂贵的优化问题 (EOP) 有价值.
- 全科医生的立方计算复杂性限制了他们的可扩展性,随着数据的增加.
- 高维度昂贵的多目标优化问题 (EMOPs) 带来了重大的计算挑战.
研究的目的:
- 为EMOP开发一个计算效率高的代孕辅助进化算法 (SAEA).
- 在高维空间中克服传统高斯过程的可扩展性限制.
- 在优化中改善勘探和开采之间的平衡.
主要方法:
- 集成多层感知子 (MLP) 分组用于子空间选择.
- 应用稀疏高斯过程 (GP) 模型,为每个目标函数优化伪输入点.
- 基于稀疏GP预测分布的自适应稀疏和多样化 (ASD) 填充标准的开发.
主要成果:
- 拟议的MLPSGP-SAEA显示出与现有的最先进的SAEA相比具有显著的竞争优势.
- 在基准套件和空气动力学设计问题上的实验结果验证了算法的有效性.
- MLP分组有效地减少了维度,稀疏的GP提高了计算效率和准确性.
结论:
- MLPSGP-SAEA为高维度EMOP提供了一个计算效率高,准确的解决方案.
- 整合MLP分组和稀疏GP有效地解决了传统GP的局限性.
- ASD填充标准有助于平衡勘探和开采,以提高优化性能.
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