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
Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy, BIOMA 2022
On Suitability of the Customized Measuring Device for Electric Motor, IECON 2022
Dynamic Computational Resource Allocation for CFD Simulations Based on Pareto Front Optimization, GECCO '22

Books & Chaptersshow all

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

Differential Evolution with Estimation of Distribution for Worst-case Scenario Optimization

M. Antoniou, G. Papa
Mathematics, 2021, Vol. 9, No. 17, pages: 22
DOI: 10.3390/math9172137

BibTex:
@Article{AntoniouPapa2021,
AUTHOR = {Antoniou, Margarita and Papa, Gregor},
TITLE = {Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization},
JOURNAL = {Mathematics},
VOLUME = {9},
YEAR = {2021},
NUMBER = {17},
ARTICLE-NUMBER = {2137},
URL = {https://www.mdpi.com/2227-7390/9/17/2137},
ISSN = {2227-7390},
DOI = {10.3390/math9172137}
}

abstract:
Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential evolution with an estimation of distribution algorithm. The algorithm has a nested form, where a differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, we estimate the distribution of the best worst solution for the best solutions found so far. The probabilistic model is used to sample part of the initial population of the scenario space differential evolution, using a priori knowledge of the previous generations. The method is compared with a state-of-the-art algorithm on both benchmark problems and an engineering application, and the related results are reported.