Decision Maker Preferences in Surrogate-based Multi-Objective Optimization: A Survey

Abstract

Multi-objective optimization problems are highly relevant in practice, and algorithms to solve these types of problems abound in the literature. This survey focuses explicitly on surrogate-based algorithms that use the decision-maker's preference information to guide the search toward the most preferred areas of the Pareto front. Considering such preferences not only facilitates the decision-making process for the user but also helps the analyst to save expensive computational budget. The way in which user preference information is handled in the algorithms differs across publications. We classify them according to the type and timing of the preference information. We provide an overview of the state-of-the-art, highlight the most important shortcomings in the literature, and present promising directions for further research.