Authors:
Siham Tassouli
and
Abdel Lisser
Affiliation:
Laboratoire des Signaux et Systèmes (L2S), CentraleSupélec, Université Paris Saclay, 3, rue Joliot Curie, 91192 Gif Sur Yvette Cedex, France
Keyword(s):
Dynamical Neural Network, Distributionally Robust Optimization, Joint Chance Constraints, Particle Swarm Optimization, Two-Timescale Systems.
Abstract:
This paper introduces a new neurodynamic duplex approach to address distributionally robust joint chance-constrained optimization problems. We assume that the constraints’ row vectors are independent, and their probability distributions belong to a specific distributional uncertainty set that is not known beforehand. Within our study, we examine two uncertainty sets for these unknown distributions. Our framework’s key feature is the use of a neural network-based method to solve distributionally robust joint chance-constrained optimization problems, achieving an almost sure convergence to the optimum without relying on standard state-of-the-art solving methods. In the numerical section, we apply our proposed approach to solve a profit maximization problem, demonstrating its performance and comparing it against existing state-of-the-art methods.