Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search

2021-07-26 · Bastien Talgorn, Stéphane Alarie, Michael Kokkolaras

We consider computationally expensive blackbox optimization problems and present a method that employs surrogate models and concurrent computing at the search step of the mesh adaptive direct search (MADS) algorithm. Specifically, we solve a surrogate optimization problem using locally weighted scatterplot smoothing (LOWESS) models to find promising candidate points to be evaluated by the blackboxes. We consider several methods for selecting promising points from a large number of points. We conduct numerical experiments to assess the performance of the modified MADS algorithm with respect to available CPU resources by means of five engineering design problems.

📄 PDF Abstract BibTeX arXiv:2107.12421

Code (0)

등록된 구현이 없습니다.

Tasks

CPU

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

2026-05-30 · Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du 외 arxiv

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems. However, their reliance on rigid and manually designed components limits their flexibility and gener…

Reinforcement Learning Ensemble Learning

Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

2022-12-13 · Qi Huang, Roy de Winter, Bas van Stein, Thomas Bäck 외

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering pr…

global-optimization Management

Surrogate-assisted parallel tempering for Bayesian neural learning

2018-11-21 · Rohitash Chandra, Konark Jain, Arpit Kapoor, Ashray Aman

Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inferenc…

Bayesian Inference Decision Making Uncertainty Quantification

Accelerating PDE Surrogates via RL-Guided Mesh Optimization

2026-03-02 · Yang Meng, Ruoxi Jiang, Zhuokai Zhao, Chong Liu 외 arxiv

Deep surrogate models for parametric partial differential equations (PDEs) can deliver high-fidelity approximations but remain prohibitively data-hungry: training often requires thousands of fine-grid simulations, each i…

Reinforcement Learning

Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis

2025-08-11 · Tomohiro Harada, Enrique Alba, Gabriel Luque arxiv

Solving complex real problems often demands advanced algorithms, and then continuous improvements in the internal operations of a search technique are needed. Hybrid algorithms, parallel techniques, theoretical advances,…