.png){kind=icon}
返回
A dual surrogate assisted evolutionary algorithm based on parallel search for expensive multi/many-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2023.110879.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Numerous optimization problems in the real world involve multi-objective and computationally expensive simulations (i.e., expensive multi-objective optimization problems). This paper purposes a dual surrogates-assisted evolutionary algorithm (SAEA) based on parallel search, termed DSAEA-PS, for this issue. Approximation and classification are two main implementation forms of surrogate models, but the existing methods of expensive multi-objective optimization only apply one kind of them, and scarce works have paid attention to combining approximation and classification to improve the optimization performance. In the proposed algorithm, to enhance the prediction accuracy and reliability, both the approximation model and classification model are applied to cooperate to provide the quality and uncertainty information of candidate solutions. Meanwhile, the parallel search based on heterogeneous multi-objective evolutionary algorithms is introduced for better exploration of the decision space. In addition, combined with the strengthened dominance relation (SDR), a sampling strategy that comprehensively considers the quality of candidate solutions and their uncertainty information is proposed. Experimental results with five peer competitors on a set of widely-used benchmark problems demonstrate the ability of DSAEA-PS. Furthermore, DSAEA-PS is adopted for a five-objective blended wing-body underwater glider design problem that involves time-consuming simulations of fluid dynamics and structural strength. A series of high-performance solutions obtained from DSAEA-PS verifies its effectiveness on engineering applications.
Keyword:
Surrogate-assisted evolutionary algorithm
Expensive multi-objective optimization
Approximation and classification
Parallel search
Strengthened dominance relation
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
A Kriging-Assisted Two-Archive Evolutionary Algorithm for Expensive Many-Objective Optimization
PREAI
分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏分享收藏A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization进化多目标优化中考虑收敛性和多样性的增强优势关系OAAI
OAAI分享收藏
查看更多内容