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.

翻译：多目标进化算法（MOEAs）已被证明能有效求解多目标优化问题（MOOPs）。然而，当应用于计算密集的工业问题时，其性能可能受到显著限制。为应对这一局限，我们提出了一种自适应代理建模方法，旨在加速先进多目标进化算法的早期收敛速度。这一点至关重要，因为它能确保求解器以相对较少的适应度函数评估次数找到最优或近似最优解，从而节省时间和计算资源。我们的方法采用双循环架构：外层循环运行（基准）宿主多目标进化算法，执行真实的适应度评估；内层循环包含一个自适应加速器，该加速器利用数据驱动的机器学习（ML）代理模型来近似适应度函数。通过与NSGA-II和MOEA/D集成，我们在31个广为人知的基准问题和一个现实世界的北海鱼类丰度建模案例研究中测试了所提方法。结果表明，通过结合高斯过程回归、一维卷积神经网络和随机森林回归，我们提出的方法能显著加速多目标进化算法在优化早期阶段的收敛速度。