Single-Objective Surrogate Models for Continuous Metaheuristics: An Overview

This paper presents a comprehensive overview of single-objective surrogate models for continuous metaheuristics, addressing computationally expensive optimization problems. Metaheuristics typically require numerous objective function evaluations that become impractical with expensive simulations. Surrogate-assisted metaheuristics address this by substituting costly evaluations with lower-cost approximations. We examine three fundamental approaches: regression models that predict exact objective function values, classification models that categorize solutions, and ranking models focusing on relative ordering. We analyze various surrogate types and discuss their strengths and limitations. We discuss structural approaches from global to local models, sample management strategies, and recent advances in ensemble methods and adaptive sampling techniques.