A dual surrogate assisted evolutionary algorithm based on parallel search for expensive multi/many-objective optimization

计算机科学 进化算法 水准点(测量) 数学优化 多目标优化 替代模型 对偶(语法数字) 集合(抽象数据类型) 最优化问题 算法 人工智能 机器学习 数学 艺术 文学类 程序设计语言 地理 大地测量学
作者
Jiangtao Shen,Peng Wang,Ye Tian,Huachao Dong
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:148: 110879-110879 被引量:3
标识
DOI:10.1016/j.asoc.2023.110879
摘要

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.
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