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Shenfield, Alex; Fleming, Peter (2011)
Publisher: International Federation of Automatic Control
Languages: English
Types: Part of book or chapter of book
The emerging paradigm of grid computing provides a powerful platform for the solution of complex and computationally expensive problems. An example of this is the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems, before using the framework in the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H-infinity loop shaping.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Baker, J.E. (1987). Reducing bias and inefficiency in the selection algorithm. In J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms, 14-21. Lawrence Erlbaum.
    • Baker, M., Buyya, R., and Laforenza, D. (2002). Grid and grid technologies for wide area distributed computing. Software: Practice and Experience, 32(15), 1437-1466.
    • Cantu´-Paz, E. and Goldberg, D.E. (1999). On the scalability of parallel genetic algorithms. Evolutionary Computation, 7(4), 429-449.
    • Chipperfield, A.J. and Fleming, P.J. (1995). Parallel genetic algorithms. In A.Y. Zomaya (ed.), Parallel And Distributed Computing Handbook, chapter 39, 1118- 1144. McGraw-Hill.
    • Fleming, P.J. and Purshouse, R.C. (2002). Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, 10, 1223 - 1241.
    • Fogarty, T.C. and Huang, R. (1991). Implementing the genetic algorithm on transputer based parallel processing systems. In H.P. Schwefel and R. M¨anner (eds.), Parallel Problem Solving from Nature 1, volume 496 of Lecture Notes in Computer Science, 145-149. Springer-Verlag, Berlin.
    • Fogel, D.B. and Ghoziel, A. (1997). A note on representations and variation operators. IEEE Transactions on Evolutionary Computation, 1(2), 159-161.
    • Fonseca, C.M. and Fleming, P.J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part I: A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 28(1), 26-37.
    • Foster, I. and Kesselman, C. (1999). The Globus Toolkit. In I. Foster and C. Kesselman (eds.), The GRID: Blueprint for a New Computing Infrastructure, chapter 11, 259-278. Morgan Kaufmann.
    • Foster, I., Kesselman, C., Nick, J.M., and Tuecke, S. (2002). Grid services for distributed system integration. IEEE Computer, 35(6), 37 - 46.
    • Foster, I., Kesselman, C., and Tuecke, S. (2001). The anatomy of the grid: Enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15(3), 200-222.
    • Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
    • Hancock, P.J.B. (1994). An empirical comparison of selection methods in evolutionary algorithms. In T.C. Fogarty (ed.), Evolutionary Computing - AISB Workshop, volume 865 of Lecture Notes in Computer Science, 80- 94. Springer-Verlag.
    • Kleinrock, L. (1969). UCLA press release. URL http://www.lk.cs.ucla.edu/LK/Bib/REPORT/press.html.
    • McFarlane, D. and Glover, K. (1990). Robust Controller Design Using Normalized Coprime Factor Plant Descriptions, volume 138 of Lecture Notes in Control and Information Sciences. Springer-Verlag.
    • Michalewicz, Z. and Fogel, D.B. (2000). How to Solve It: Modern Heuristics. Springer.
    • Mu¨hlenbein, H. and Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm I: Continuous parameter optimization. Evolutionary Computation, 1(1), 25-49.
    • Nelson, R.C. (1998). Flight Stability and Automatic Control. McGraw-Hill, second edition.
    • Shenfield, A., Fleming, P.J., Allan, J., and Kadirkamanathan, V. (2010). Optimisation of maintenance scheduling strategies on the grid. Annals of Operations Research, 180(1), 213 - 231.
    • Skogestad, S. and Postlethwaite, I. (1996). Multivariable feedback control - Analysis and Design. John Wiley & Sons.
    • Tang, X. and Chanson, S.T. (2000). Optimizing static job scheduling in a network of heterogeneous computers. In Proceedings of the International Conference on Parallel Processing, 373-382.
    • The White Rose University Consortium (2010). The white rose grid website. URL http://www.wrgrid.org.uk/. Viewed 20 September 2010.
    • Zames, G. (1981). Feedback and optimal sensitivity: model reference transformations, multiplicitive seminorms, and approximate inverse. IEEE Transactions on Automatic Control, 26(2), 301-320.
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