The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization

Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, including diffusion models, show strong performance under hypervolume, yet their behavior under other established MOO metrics is less understood. We show that generative methods systematically underperform evolutionary alternatives with respect to other metrics, such as generational distance. We relate this failure mode to the offline-frontier shift, i.e., the displacement of the offline dataset from the Pareto front, which acts as a fundamental limitation in offline MOO. We argue that overcoming this limitation requires out-of-distribution sampling in objective space (via an integral probability metric) and empirically observe that generative methods remain conservatively close to the offline objective distribution. Our results position offline MOO as a distribution-shift--limited problem and provide a diagnostic lens for understanding when and why generative optimization methods fail.

Adaptive Surrogate-Based Strategy for Accelerating Convergence Speed when Solving Expensive Unconstrained Multi-Objective Optimisation Problems

Multi-Objective Evolutionary Algorithms (MOEAs) have proven effective at solving Multi-Objective Optimisation Problems (MOOPs). However, their performance can be significantly hindered when applied to computationally intensive industrial problems. To address this limitation, we propose an adaptive surrogate modelling approach designed to accelerate the early-stage convergence speed of state-of-the-art MOEAs. This is important because it ensures that a solver can identify optimal or near-optimal solutions with relatively few fitness function evaluations, thereby saving both time and computational resources. Our method employs a two-loop architecture. The outer loop runs a (baseline) host MOEA which carries out true fitness evaluations. The inner loop contains an Adaptive Accelerator that leverages data-driven machine learning (ML) surrogate models to approximate fitness functions. Integrated with NSGA-II and MOEA/D, our approach was tested on 31 widely known benchmark problems and a real-world North Sea fish abundance modelling case study. The results demonstrate that by incorporating Gaussian Process Regression, one-dimensional Convolutional Neural Networks, and Random Forest Regression, our proposed approach significantly accelerates the convergence speed of MOEAs in the early phases of optimisation.