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 recent

A Framework for Applying Data-Driven AI/ML Models in Reliability, Recent Advances in Microelectronics Reliability, Springer (2024)
Multilevel optimisation, Optimization Under Uncertainty with Applications to Aerospace Engineering, Springer (2021)
Parameter Control in Evolutionary Optimisation, Optimization Under Uncertainty with Applications to Aerospace Engineering, Springer (2021)

Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy

M. Antoniou, G. Papa
Proc. Bioinspired Optimization Methods and Their Applications
Maribor, Slovenia: 17-18 November 2022
pages: 84-91
DOI: https://doi.org/10.1007/978-3-031-21094-5_7

BibTex:
@inproceedings{antoniou2022evaluation, title={Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy}, author={Antoniou, Margarita and Papa, Gregor}, booktitle={Bioinspired Optimization Methods and Their Applications: 10th International Conference, BIOMA 2022, Maribor, Slovenia, November 17--18, 2022, Proceedings}, pages={84--98}, year={2022}, organization={Springer} }

abstract:
When optimization is applied in real-world applications, optimal solutions that do not take into account uncertainty are of limited value, since changes or disturbances in the input data may reduce the quality of the solution. One way to find a robust solution and consider uncertainty is to formulate the problem as a min-max optimization problem. Min-max optimization aims to identify solutions which remain feasible and of good quality under even the worst possible scenarios, i.e., realizations of the uncertain data, formulating a nested problem. Employing hierarchical evolutionary algorithms to solve the problem requires numerous function evaluations. Nevertheless, Evolutionary Algorithms can be easily parallelized. This work investigates a parallel model for differential evolution using SciPy, to solve general unconstrained min-max problems. A differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, the design level optimization is parallelized. The performance of the algorithm is evaluated for a different number of cores and different dimensionality of four benchmark test functions. The results show that, when the right parameters of the algorithm are selected, the parallelization can be of high benefit to a nested differential evolution.