## 1 Introduction
Surrogates, such as response surface models or Kriging models [10], can significantly expedite the optimization process by substituting costly evaluations with less expensive predictions [11]. [PERSON] et al. [12] presented an artificial neural network residual kriging-based surrogate model for the shape and size optimization of a stiffened panel. [PERSON] and [PERSON] [13] used a surrogate-assisted evolution strategy for constrained multi-objective optimization. Their approach used cubic Radial Basis Function (RBF) surrogate models to assist the optimization by predicting the objective and constraint function values. [PERSON] et al. [14] created a surrogate-assisted particle swarm optimization algorithm for expensive multi-objective optimization. [PERSON] et al. [15] examined the surrogate-based multi-objective optimization of the stiffness and buckling load of a composite laminate plate with curvilinear fiber paths. [PERSON] and [PERSON] [16; 17] developed a deep-learning-based surrogate model that can provide the optimum topologies of 2D and 3D structures. Despite their advantages, surrogate models require substantial initial training data to achieve high accuracy, and their performance can degrade in large, complex design spaces characterized by numerous local optima [15]. Moreover, the effectiveness of surrogate-based approaches is highly dependent on the quality of the model, which can vary significantly across different regions of the search space. Consequently, there remains a need for more adaptive and efficient methods to improve the computational efficiency of MOO. [PERSON] et al. [18] presented an adaptive response surface method (ARSM) for improving the global optimization of computation-intensive design problems. [PERSON] et al. [19] described the development of an adaptive response surface method for the optimization of computationally expensive problems. Their surrogate model is adapted and improved during the optimization and is not trained from a pre-defined number of design experiments. Active learning, a sub-domain of machine learning, offers a promising alternative to traditional surrogate-based approaches. It is an adaptive machine learning technique that incrementally improves a machine learning model by selectively querying the most informative data points. This approach dynamically updates the surrogate model based on the model's uncertainty, rather than relying on a static, pre-trained model. [PERSON] et al. [20] presented committee-based active learning for the surrogate-assisted particle swarm optimization of computationally expensive problems. [PERSON] and [PERSON] [21] effectively used active learning in single-objective optimization problems to reduce the number of expensive evaluations by focusing on uncertain regions of the search space. In this study, we apply active learning to multi-objective optimization problems. Our approach integrates active learning into the MOO framework to adaptively refine the surrogate model during optimization. By querying new data points only when the model is uncertain, we aim to reduce the number of constraint evaluations and enhance the computational efficiency of MOO algorithms. Uncertainty is quantified by calculating the difference in the predicted probabilities from the active learner, which we refer to as the confidence parameter. This parameter helps identify areas where the model is less certain, guiding the selection of new data points. Section 2 presents the details of the methodology of the proposed approach. Section 3 shows the application of the approach to different benchmark constrained optimization problems. The results demonstrate more than 50% reductions in constraint evaluations while maintaining high solution quality. The results of our case studies align well with previous research, particularly in terms of achieving competitive Inverted Generational Distance (IGD) values and maintaining solution quality.