Expensive Many-objective Evolutionary Algorithm Based on Radial Space Division

被引：0
|
作者：
Gu Q.-H. [1 ,2 ]
Zhou Y.-F. [1 ]
Li X.-X. [1 ]
Ruan S.-L. [2 ]
机构：
[1] School of Management, Xi＇an University of Architecture and Technology, Xi＇an
[2] School of Resource Engineering, Xi＇an University of Architecture and Technology, Xi＇an
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 10期
基金：
中国国家自然科学基金;
关键词：
double archives management strategy; Expensive many-objective optimization problem; Gaussian process; radial projection;
D O I：
10.16383/j.aas.c200791
中图分类号：
学科分类号：
摘要：
In order to solve the problem of many-objective optimization that it is difficult to establish an accurate mathematical model or the actual evaluation experiment is expensive, an expensive many-objective evolutionary algorithm based on radial space division is proposed. First, the algorithm uses Gaussian regression as a surrogate model to approximate the objective function; Second the individuals in the objective space are projected to the radial space, and the individuals with higher contributions to the population are retained through the objective space and radial space information; Third the position distribution of the individuals in the radial space determines which individuals should be selected for real evaluation in the next step; Finally, a double archives management strategy is adopted to maintain the quality of the surrogate model. The results of numerical experiments and real problems show that compared with five advanced algorithms, the algorithm proposed in this paper can provide higher quality solutions when solving expensive many-objective optimization problems. © 2022 Science Press. All rights reserved.
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页码：2564 / 2584
页数：20
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