# Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems

arXiv: https://arxiv.org/abs/2402.16455 · HTML: https://mlsanity.com/paper/2402.16455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions](https://mlsanity.com/paper/2106.04618/) (2021)
- [Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems](https://mlsanity.com/paper/2011.03431/) (2020)
- [Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review](https://mlsanity.com/paper/1806.04563/) (2018)
- [Black-box Combinatorial Optimization using Models with Integer-valued Minima](https://mlsanity.com/paper/1911.08817/) (2019)
- [A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms](https://mlsanity.com/paper/2004.09969/) (2020)
- [Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym](https://mlsanity.com/paper/2310.00077/) (2023)
- [Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic](https://mlsanity.com/paper/2012.07188/) (2020)
- [Combining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems](https://mlsanity.com/paper/2204.14236/) (2022)

## Built on by (most cited first, 1 of 1)

- [SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations](https://mlsanity.com/paper/2511.00685/) (2025)

## Abstract (arXiv)

Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods.