Advertisement

Real World System Architecture Design Using Multi-criteria Optimization: A Case Study

  • Rajesh Kudikala
  • Andrew R. Mills
  • Peter J. Fleming
  • Graham F. Tanner
  • Jonathan E. Holt
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 227)

Abstract

System architecture design using multi-criteria optimization is demonstrated using a case study of an aero engine health management (EHM) system. A design process for optimal deployment of EHM system functional operations over physical architecture component locations, e.g., on-engine, on-aircraft and on-ground, is described. The EHM system architecture design needs to be optimized with respect to many qualitative criteria in terms of operational attributes within the constraints of resource limitations. In this paper the system architecture design problem is formulated as a multi-criteria optimization problem. Considering the large discrete search space of decision variables and many-objective functions and constraints, an evolutionary multi-objective genetic algorithm along with a progressive preference articulation technique, is used for solving the optimization problem. The optimization algorithm found a family of Pareto solutions which provided valuable insight into design trade-offs. Using the progressive preference articulation technique, the optimization search can be focused for the industrial decision maker on to a region of interest in the objective space. Performance of the proposed method is evaluated using various test metrics. Using this approach it was possible to identify the most significant design constraints (“hot spots”) and the opportunities afforded by either the relaxation or the tightening of these constraints, along with their performance implications.

Keywords

System architecture design multi-criteria optimization many-objective optimization preference articulation genetic algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adra, S.F., Griffin, I., Fleming, P.J.: A comparative study of progressive preference articulation techniques for multiobjective optimisation. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 908–921. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Armstrong, M., de Tenorio, C., Garcia, E., Mavris, D.: Function based architecture design space definition and exploration. In: 26th International Congress of Aeronautical SciencesGoogle Scholar
  3. 3.
    Branke, J., Deb, K.: Integrating user preferences into evolutionary multi-objective optimization. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation. STUDFUZZ, vol. 167, pp. 461–478. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Crawley, E., de Weck, O., Eppinger, S., Magee, C., Moses, J., Seering, W., Schindall, J., Wallace, D., Whitney, D.: The influence of architecture in engineering systems. Engineering Systems Monograph (2004)Google Scholar
  5. 5.
    Cvetkovic, D., Parmee, I.: Preferences and their application in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 6(1), 42–57 (2002)CrossRefGoogle Scholar
  6. 6.
    Deb, K.: Multi-objective optimization using evolutionary algorithms, vol. 16. John Wiley & Sons, Hoboken (2001)Google Scholar
  7. 7.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  8. 8.
    Fleming, P.J., Purshouse, R.C., Lygoe, R.J.: Many-objective optimization: An engineering design perspective. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 14–32. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Fonseca, C., Fleming, P.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, vol. 1, pp. 416–423 (1993)Google Scholar
  10. 10.
    Fonseca, C., Fleming, P.: Multiobjective Genetic Algorithms Made Easy: Selection Sharing and Mating Restriction. In: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 45–52. IET (1995)Google Scholar
  11. 11.
    Fonseca, C., Fleming, P.: Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms. I. A Unified Formulation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 28(1), 26–37 (1998)CrossRefGoogle Scholar
  12. 12.
    Goldberg, D.: Genetic algorithms in search, optimization, and machine learning (1989)Google Scholar
  13. 13.
    Gries, M.: Methods for evaluating and covering the design space during early design development. Integration, the VLSI Journal 38(2), 131–183 (2004)Google Scholar
  14. 14.
    Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary Many-Objective Optimization: A Short Review. In: IEEE Congress on Evolutionary Computation, pp. 2419–2426 (June 2008)Google Scholar
  15. 15.
    Martens, A., Koziolek, H., Becker, S., Reussner, R.: Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms. In: Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering, pp. 105–116. ACM (2010)Google Scholar
  16. 16.
    Pimentel, A., Erbas, C., Polstra, S.: A systematic approach to exploring embedded system architectures at multiple abstraction levels. IEEE Transactions on Computers 55(2), 99–112 (2006)CrossRefGoogle Scholar
  17. 17.
    Purshouse, R., Fleming, P.: On the Evolutionary Optimization of Many Conflicting Objectives. IEEE Transactions on Evolutionary Computation 11(6), 770–784 (2007)CrossRefGoogle Scholar
  18. 18.
    Selva, D., Crawley, E.: Integrated assessment of packaging architectures in earth observing programs. In: IEEE Aerospace Conference, pp. 1–17. IEEE (2010)Google Scholar
  19. 19.
    Tanner, G., Crawford, J.: An integrated engine health monitoring system for gas turbine aero-engines. IEE Seminar on Aircraft Airborne Condition Monitoring 2003(10203), 5 (2003)CrossRefGoogle Scholar
  20. 20.
    Thompson, H., Chipperfield, A., Fleming, P., Legge, C.: Distributed aero-engine control systems architecture selection using multi-objective optimisation. Control Engineering Practice 7(5), 655–664 (1999)CrossRefGoogle Scholar
  21. 21.
    Veldhuizen, D.A.V.: Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Tech. rep., Air Force Institute of Technology (1999)Google Scholar
  22. 22.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Tech. Rep. 103, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland (2001)Google Scholar
  23. 23.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rajesh Kudikala
    • 1
  • Andrew R. Mills
    • 1
  • Peter J. Fleming
    • 1
  • Graham F. Tanner
    • 2
  • Jonathan E. Holt
    • 2
  1. 1.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK
  2. 2.Rolls-Royce plcDerbyUK

Personalised recommendations