Data-Driven Multi-Objective Optimization: Analysis of Current Methods and Techniques

Multi-objective optimization holds practical relevance, as the majority of real-world problems involve multiple conflicting objectives. This study aims to analyze data-driven multi-objective optimization methods, focusing on key aspects such as the employed algorithms, incorporation of machine learning techniques as surrogate models, number of evaluated objectives, consideration of model interpretability, and metrics used to evaluate the performance of the algorithms. This research distinguishes itself by providing a review and analysis of dataguided multi-objective optimization algorithms over the last six years. The results offer valuable guidance to the scientific community seeking to address complex problems using datadriven multi-objective optimization algorithms.