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Genetic Algorithm Optimization of SoS Meta-Architecture Attributes for Fuzzy Rule Based Assessments
Procedia Computer Science
  • Andrew Renault
  • Cihan H. Dagli, Missouri University of Science and Technology
Abstract

The analysis of an acknowledged systems of systems (SoS) meta-architecture requires a preliminary method for potential trade space exploration to ensure compliance to evolving capability requirements. It is important to assess the SoS meta-architecture concept to ensure that it satisfies all stakeholder needs and requirements in the early stages of development. There are numerous linguistic terms called key performance attributes (KPAs) that could be used to assess the different aspects of the architectures capabilities, however, too many KPAs could complicate the assessment. The initial population of suitable KPAs is reduced through non-derivative based optimization employed by a genetic algorithm (GA) that generates the ideal KPA candidates though optimal rank selection. A Mamdani-type rule based fuzzy inference system (MRBFIS) is then used to make a fuzzy assessment of the SoS meta-architecture concept using GA optimized and assessed KPAs as MRBFIS inputs. The MRBFIS is a beneficial addition to an architecture assessment because it enables a nonlinear output that allows a more dynamic and adjustable assessment. The integrated assessment method detailed in this paper utilizes the GA optimized KPAs and the MRBFIS to provide a valuable fuzzy assessment of SoS meta-architecture concepts to determine if the architecture is feasible and acceptable.

Meeting Name
Complex Adaptive Systems (2016: Nov. 2-4, Los Angeles, CA)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
  • Adaptive systems,
  • Fuzzy systems,
  • Genetic algorithms,
  • Inference engines,
  • Linguistics,
  • Space research,
  • System of systems,
  • Systems engineering,
  • Architecture assessment,
  • Assessment,
  • Capability requirements,
  • Genetic-algorithm optimizations,
  • Integrated assessment,
  • Rule-based fuzzy inference system,
  • Systems of systems,
  • Trade space explorations,
  • Fuzzy inference,
  • Meta-architecture,
  • Rule based fuzzy inference systems
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2016 Elsevier, All rights reserved.
Creative Commons Licensing
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Publication Date
11-1-2016
Citation Information
Andrew Renault and Cihan H. Dagli. "Genetic Algorithm Optimization of SoS Meta-Architecture Attributes for Fuzzy Rule Based Assessments" Procedia Computer Science Vol. 95 (2016) p. 95 - 102 ISSN: 1877-0509
Available at: http://works.bepress.com/cihan-dagli/167/