广东工业大学学报2024，Vol.41Issue(6)：69-79,11.DOI:10.12052/gdutxb.240032
回归分类协同昂贵约束多目标优化算法
Regression Classification Collaborative Expensive Constrained Multi-objective Optimisation Algorithm
摘要
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.关键词
昂贵约束/多目标优化/代理辅助进化算法/分类器与回归器协同Key words
expensive constrained/multi-objective optimization/surrogate assisted evolutionary algorithm/classifier and regressor collaboration分类
信息技术与安全科学引用本文复制引用
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):69-79,11.基金项目
国家自然科学基金资助项目(62272108) （62272108）
