Abstract
Large 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 value. 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.
Recommended Citation
C. O. Adler and C. H. Dagli, "Study of the Use of a Genetic Algorithm to Improve Networked System-of-systems Resilience," Procedia Computer Science, vol. 36, pp. 49 - 56, Elsevier, Jan 2014.
The definitive version is available at https://doi.org/10.1016/j.procs.2014.09.036
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Complex system; Enabling systems; Failure modes; Genetic algorithm; Systems architecture analysis; Systems-of-systems
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Jan 2014
