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.

Key words: expensive constrained, multi-objective optimization, surrogate assisted evolutionary algorithm, classifier and regressor collaboration