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Multi-objective differential evolution algorithm based on partial reinforcement learning intelligence for engineering design problems and physics-informed neural networks

delete2026-03-27
delete0
PRE
AI
J
Jianqiang Yang
F
Fu Yan *
张
张静 (Jing Zhang)
C
Changgen Peng
Y
Yuling Chen
DOI：10.1016/j.aei.2026.104608delete
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摘要

摘要

En 中文
本文提出了一种创新的Multi-Objective Partial Reinforcement Learning Evolutionary Algorithm（MODEPRL）。与现有的基于强化学习的差分进化（DE）变体主要关注策略选择或参数自适应不同，MODEPRL通过部分强化学习（PRL）模拟人类或动物社会群体中观察到的智能行为。其核心创新在于将学习者的决策行为与自适应评分表相关联，使个体能够基于由正负强化定义的环境刺激自主调整其行为。该机制使学习者能够协同提升整体优化性能。在27个机械设计问题和物理信息神经网络上的数值模拟表明，MODEPRL在全局搜索能力、解精度和收敛速度方面优于当前最先进算法。
Keyword:
Multi-Objective Differential Evolution
Partial Reinforcement Learning
Adaptive Score Table
Engineering Design Problems
Physics-Informed Neural Networks

期刊

Advanced Engineering Informatics 封面图
Advanced Engineering Informatics
IF:
9.9
论文数:
4.4K
被引数:
1.7W

机构

G
guizhou university
学者数:
2.5W
论文数: 1.3W
被引数: 15
引用论文

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