Study of the Use of a Genetic Algorithm to Improve Networked System-of-Systems Resilience

AbstractLarge scale failures or degradation resulting from smaller initial failures or disruptions in networked system-of-systems are an issue in multiple areas - for example cascading failures in electrical power grids or large disruptions in national air traffic due to local or regional weather conditions. The system architecture can have a significant impact on system-of-systems susceptibility to large scale failures.The study presented in this paper uses a simple interdependent networked system-of-systems failure model, integrated into a unique objective function that addresses both the overall level of failure and the rate of failure progression, and a genetic algorithm to demonstrate an integrated failure modeling based optimization method to select system-of-systems architectures for improved resiliency.The results for the integrated failure model/genetic algorithm model results converged rapidly to a steady state values. This initial integration shows a possible path forward to more sophisticated model integration and optimization and demonstrates a basic level of feasibility for this general approach