一个基于高斯模型的多目标进化算法,使用人口指导重量向量的进化策略
Xiaofang Guo1, Yuping Wang1, Haonan Zhang1
1School of Sciences, Xi'an Technological University, Xi'an 710000, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了一种基于高斯模型的多目标进化算法 (ALGM-MOEA),以提高搜索效率和预测准确度. 这种新的方法动态调整各种帕雷托前线形状的搜索方向,在基准问题上展示了竞争性表现.
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:
- 多目标进化算法 (MOEA) 经常依赖交叉运算符来生成后代.
- 现有的MOEA面临着适应各种帕雷托前线 (PF) 形状的挑战.
- 基于反向模型的MOEA (IM-MOEA) 提供了一个替代的计算方案.
研究的目的:
- 提出一种基于高斯模型的多目标进化算法 (ALGM-MOEA).
- 提高MOEA的搜索效率和预测准确度.
- 为了有效地处理各种 PF 形状的多目标问题.
主要方法:
- 开发了一种以人口为导向的重量向量演变策略,以根据PF分布动态调整搜索方向.
- 实施了基于积极学习的训练样本选择,用于高斯过程反向模型.
- 利用高斯过程模型来预测后代.
主要成果:
- 拟议的以人口为导向的重量向量演变策略有效地适应了不同的PF形状.
- 积极学习提高了高斯过程反向模型的预测准确度.
- 在基准多目标优化问题上,ALGM-MOEA表现出了竞争力.
结论:
- ALGM-MOEA提供了一种强大的方法,用于在各种PF形状中实现多目标优化.
- 积极学习和以人口为导向的战略的整合改善了MOEA的绩效.
- 拟议的方法为传统的MOEA设计提供了有竞争力的替代方案.
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