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

• URL: http://arxiv.org/abs/2601.21885v1
• Date: Thu, 29 Jan 2026 15:46:52 GMT
• Title: Adaptive Surrogate-Based Strategy for Accelerating Convergence Speed when Solving Expensive Unconstrained Multi-Objective Optimisation Problems
• Authors: Tiwonge Msulira Banda, Alexandru-Ciprian Zăvoianu,
• Abstract summary: We propose an adaptive surrogate modelling approach designed to accelerate the early-stage convergence speed of state-of-the-art MOEAs.<n>This is important because it ensures that a solver can identify optimal or near-optimal solutions with relatively few fitness function evaluations.<n>Our approach was tested on 31 widely known benchmark problems and a real-world North Sea fish abundance modelling case study.
• Score: 41.99844472131922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• An Experimental Approach for Running-Time Estimation of Multi-objective Evolutionary Algorithms in Numerical Optimization [16.66619776655723]
  We propose an experimental approach for estimating upper bounds on the running time of MOEAs without algorithmic assumptions.<n>We conduct comprehensive experiments on five representative MOEAs using the ZDT and DTLZ benchmark suites.<n>Results demonstrate the effectiveness of our approach in estimating upper bounds on the running time without requiring algorithmic or problem simplifications.
  arXiv  Detail & Related papers  (2025-07-03T07:06:14Z)
• Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators [5.565261874218803]
  This paper demonstrates the power of differentiability for solving MOO problems using a Deep Differentiable Reinforcement Learning algorithm in particle accelerators. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraint for energy requirements of the beam.
  arXiv  Detail & Related papers  (2024-11-07T15:55:05Z)
• Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II [52.083337333478674]
  This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW) The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL.
  arXiv  Detail & Related papers  (2024-07-18T02:46:06Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls [0.4543820534430522]
  We show that theBO-LCB algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered. We also show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%. We demonstrate a two-orders-of-magnitude speedup for the design optimization process when the surrogate model is used.
  arXiv  Detail & Related papers  (2023-04-24T19:52:42Z)
• Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization [3.012067935276772]
  Generalized data-driven evolutionary algorithm (GDDE) is proposed to reduce the number of simulation runs on well-placement and control optimization problems. Probabilistic neural network (PNN) is adopted as the classifier to select informative and promising candidates.
  arXiv  Detail & Related papers  (2022-06-07T09:07:49Z)
• Multi-Agent Deep Reinforcement Learning in Vehicular OCC [14.685237010856953]
  We introduce a spectral efficiency optimization approach in vehicular OCC. We model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method.
  arXiv  Detail & Related papers  (2022-05-05T14:25:54Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.