Regression Classification Collaborative Expensive Constrained Multi-objective Optimisation Algorithm

Hu Xiao-min; Wang Bing-hai; Huang Jia-wen; Gong Chao-fu; Li Min

doi:10.12052/gdutxb.240032

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Regression Classification Collaborative Expensive Constrained Multi-objective Optimisation Algorithm

更新时间：2026-05-27

- Regression Classification Collaborative Expensive Constrained Multi-objective Optimisation Algorithm
- Vol. 41, Issue 6, Pages: 69-79(2024)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240032
  CLC： TP301.6
- Published：2024
- 稿件说明：
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胡晓敏, 王炳海, 黄佳玟, 龚超富, 李敏. 回归分类协同昂贵约束多目标优化算法[J]. 广东工业大学学报, 2024, 41(6): 69-79.

Hu Xiao-min, Wang Bing-hai, Huang Jia-wen, et al. Regression Classification Collaborative Expensive Constrained Multi-objective Optimisation Algorithm[J]. 2024, 41(6): 69-79.
胡晓敏, 王炳海, 黄佳玟, 龚超富, 李敏. 回归分类协同昂贵约束多目标优化算法[J]. 广东工业大学学报, 2024, 41(6): 69-79. DOI： 10.12052/gdutxb.240032.

Hu Xiao-min, Wang Bing-hai, Huang Jia-wen, et al. Regression Classification Collaborative Expensive Constrained Multi-objective Optimisation Algorithm[J]. 2024, 41(6): 69-79. DOI： 10.12052/gdutxb.240032.

摘要

现有基于代理模型的昂贵约束多目标优化算法存在两个问题，即使用回归模型拟合约束时带来的误差影响算法的搜索方向，以及目标函数存在不可拟合的情况时，回归模型拟合效果差。为解决这两个问题，提出一种分类模型与回归模型协同的昂贵约束多目标进化优化算法。该方法使用分类模型对搜索空间进行粗略划分，指导算法快速进入可行区域，减弱约束拟合误差的影响。使用回归模型在可行区域内优化目标函数。两种模型协同工作，分类模型提供概括的搜索方向，回归模型进行精细建模。这种模型的融合，既考虑了约束误差对算法的影响，也综合了目标函数的可拟合性问题，能更全面准确地描绘复杂问题的特征，从而提高算法的求解效率和效果，为进一步提升基于代理模型的昂贵约束多目标优化提供了一种协同建模的有效途径。

Abstract

The existing expensive constrained multi-objective optimization algorithms based on surrogate models face two main issues. Firstly

the use of regression models to fit constraints introduces errors that affect the algorithm's search direction. Secondly

when the objective function is non-fittable

the performance of the regression model for fitting is poor. To address these issues

a collaborative expensive constrained multi-objective evolutionary optimization algorithm is proposed

which combines a classification model with a regression model. This method employs the classification model to roughly divide the search space

guiding the algorithm to quickly enter the feasible region and reducing the impact of constraint fitting errors. The regression model is then used to optimize the objective function within the feasible region. The collaboration of the two models allows the classification model to provide a general search direction while the regression model performs detailed modeling. This fusion of models not only considers the impact of constraint errors on the algorithm but also comprehensively addresses the fittability of the objective function

enabling a more comprehensive and accurate depiction of the characteristics of complex problems. As a result

it enhances the efficiency and effectiveness of the algorithm

providing an effective approach for further improving expensive constrained multi-objective optimization based on surrogate models.

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Keywords

references

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