.png){kind=icon}
返回
Considering spatiotemporal evolutionary information in dynamic multi-objective optimisation
.png){kind=icon}
OA
AI
DOI：10.1049/cit2.12249.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi-objective optimisation (DMO), are challenging since the sampling space is time- and space-varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO. In the present study, a sliding-time-window-based population clustering method (SPC) is proposed to effectively solve dynamic multi-objective optimisation problems (DMOPs). In the SPC, the knowledge is provided by saving historical data in the temporal dimension, and a spectral clustering method is used to divide the saved data into multiple neighbourhood subspaces for preserving population diversity in the spatial dimension. The SPC is incorporated into the RM-MEDA and is compared with other recently proposed state-of-the-art dynamic multi-objective evolutionary algorithms (DMOEAs) on 14 DMOPs introduced in IEEE CEC2018. Simulation results demonstrate that the proposed method is capable of enhancing the tracking performance of the RM-MEDA in the dynamically changing environments. Additionally, the SPC is utilised to solve an actual dynamic multi-objective translation control problem of an immersed tunnel element. Results show that the proposed SPC outperforms the knee point-based transfer learning method in terms of both computational cost and tracking performance.
Keyword:
dynamic multi-objective optimization
dynamic optimization
evolutionary computation
high-performing computing
immersed tunnel element
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
669
被引数:
2.4K
机构
引用论文
.png){kind=icon}
分享收藏
Solving dynamic multi-objective problems with an evolutionary multi-directional search approach
PREAI
分享收藏A heredity-based adaptive variation operator for reinitialization in dynamic multi-objective problems基于遗传的自适应变异算子，用于动态多目标问题的重新初始化
PREAI
分享收藏
查看更多内容