Margarita Antoniou

Journal articlesshow recent

δ-perturbation of bilevel optimization problems: An error bound analysis, Oper. Res. Perspect. (2024)
Differential Evolution with Estimation of Distribution for Worst-case Scenario Optimization, MATHEMATICS (2021)
Multi-objective approaches to ground station scheduling for optimization of communication with satellites, OPTIM ENG (2021)

Conference papersshow all

An evolutionary approach to pessimistic bilevel optimization problems, BILEVEL 23
An evolutionary approach to pessimistic bilevel optimization problems, BILEVEL 2023

Books & Chaptersshow all

A Framework for Applying Data-Driven AI/ML Models in Reliability, Recent Advances in Microelectronics Reliability, Springer (2024)

Dynamic Computational Resource Allocation for CFD Simulations Based on Pareto Front Optimization

G. Petelin, M. Antoniou, G. Papa
Proc. Genetic and Evolutionary Computation Conference
Boston, USA: 9-13 July 2022
pages: 1666–1673
DOI: 10.1145/3520304.3534033

BibTex:
@inproceedings{10.1145/3520304.3534033,
author = {Petelin, Ga\v{s}per and Antoniou, Margarita and Papa, Gregor},
title = {Dynamic Computational Resource Allocation for CFD Simulations Based on Pareto Front Optimization},
year = {2022},
isbn = {9781450392686},
publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3520304.3534033},
doi = {10.1145/3520304.3534033},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {1666–1673},
numpages = {8},
keywords = {computational fluid dynamics, resource allocation, high-performance computing, multi-objective optimization},
location = {Boston, Massachusetts},
series = {GECCO '22}
}

abstract:
Computational Fluid Dynamics (CFD) simulations can be extremely computationally demanding and usually rely on the use of High-performance computing (HPC) using both CPU and GPU resources. Modeling the behavior of bubbles size distribution often leads to a symmetric function evaluation problem. This paper proposes a dynamic computational resource allocation, based on Pareto-optimal solutions. The solutions are obtained from the formulation of the resource-constrained symmetric function evaluation problem as a multi-objective problem. After the Pareto-front is obtained, we suggest a dynamic selection method of the solutions that utilize the existing resources. To solve the multi-objective problem ϵ-MOEA, an algorithm known to obtain good diversity of Pareto-front solutions, is applied. As the problem formulated is new, brute-force search and two-specifically designed for this problem-heuristics are implemented and tested to serve as baselines. The methods are tested and compared to three dimensionalities of the problem. The results showed that ϵ-MOEA can successfully approximate the Pareto-front, allowing to utilize the resources optimally at each simulation time-step.