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Shenfield, Alex; Fleming, Peter (2011)
Publisher: International Federation of Automatic Control
Languages: English
Types: Article
Subjects:
Coupling conventional controller design methods, model based controller synthesis and simulation, and multi-objective evolutionary optimisation methods frequently results in an extremely computationally expensive design process. However, the emerging paradigm of grid computing provides a powerful platform for the solution of such problems by providing transparent access to large-scale distributed high-performance compute resources. As well as substantially speeding up the time taken to find a single controller design satisfying a set of performance requirements this grid-enabled design process allows a designer to effectively explore the solution space of potential candidate solutions. An example of this is in 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 which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H ∞ 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: Grefenstette, J. J. (Ed.), Proceedings of the Second International Conference on Genetic Algorithms. Lawrence Erlbaum., pp. 14-21.
    • Baker, M., Buyya, R., Laforenza, D., 2002. Grid and grid technologies for wide area distributed computing. Software: Practice and Experience 32 (15), 1437-1466.
    • Berman, F., Wolski, R., Figueira, S., Schopf, J., Shao, G., 1996. Application-level scheduling on distributed heterogeneous networks. In: Supercomputing '96.
    • Beyer, H. G., 1995. Toward a theory of evolution strategies: Selfadaptation. Evolutionary Computation 3 (3), 311 - 347.
    • Bullock, S., Cartlidge, J., Thompson, M., 2002. Prospects for computational steering in evolutionary computation. In: Bilotta, E., Groß, D., Smith, T., Lenaerts, T., Bullock, S., Lund, H. H., Bird, J., Watson, R., Pantano, P., Pagliarini, L., Abbass, H., Standish, R., Bedau, M. (Eds.), Artificial Life VIII Workshop Proceedings. MIT Press, pp. 131-137.
    • Cantu´-Paz, E., Goldberg, D. E., 1999. On the scalability of parallel genetic algorithms. Evolutionary Computation 7 (4), 429-449.
    • Capi, G., 2008. Evolution of efficient neural controllers for robot multiple task performance - a multiobjective approach. In: IEEE international conference on Robotics and Automation 2008. pp. 2195 -2200.
    • Chappell, D. A., Jewell, T., 2002. Java Web Services. O'Reilly.
    • Chipperfield, A. J., Fleming, P. J., 1995. Parallel genetic algorithms. In: Zomaya, A. Y. (Ed.), Parallel And Distributed Computing Handbook. McGraw-Hill, Ch. 39, pp. 1118-1144.
    • Deb, K., Zope, P., Jain, A., 2003. Distributed computing of pareto optimal solutions with evolutionary algorithms. In: Proceedings of the Second International Conference on Evolutionary MultiCriteria Optimisation (EMO2003). Springer-Verlag, pp. 534-549.
    • Farina, M., Amato, P., 2004. A fuzzy definition of “optimality” for many-criteria optimization problems. IEEE Transactions on System, Man and Cybernetics - Part A: Systems and Humans 34 (3), 315-326.
    • Fleming, P. J., Purshouse, R. C., 2002. Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice 10, 1223-1241.
    • Fleming, P. J., Purshouse, R. C., Lygoe, R. J., 2005. Many objective optimization: An engineering perspective. In: Coello, C. A. C., Aguirre, A. H., Zitzler, E. (Eds.), Proceedings of the International Conference on Evolutionary Multi-Objective Optimization (EMO2005). Vol. 3470 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 14-32.
    • Fogarty, T. C., Huang, R., 1991. Implementing the genetic algorithm on transputer based parallel processing systems. In: Schwefel, H.- P., M¨anner, R. (Eds.), Parallel Problem Solving from Nature 1. Vol. 496 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 145-149.
    • Fogel, D. B., Ghoziel, A., 1997. A note on representations and variation operators. IEEE Transactions on Evolutionary Computation 1 (2), 159-161.
    • Fonseca, C. M., 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., Kesselman, C., 1999. The Globus Toolkit. In: Foster, I., Kesselman, C. (Eds.), The GRID: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, Ch. 11, pp. 259-278.
    • Foster, I., Kesselman, C., Nick, J. M., Tuecke, S., 2002. Grid services for distributed system integration. IEEE Computer 35 (6), 37 - 46.
    • Foster, I., Kesselman, C., Tuecke, S., 2001. The anatomy of the grid: Enabling scalable virtual organizations. International Journal of Supercomputer Applications 15 (3), 200-222.
    • Garcia, J. J. V., Garay, V. G., Gordo, E. I., Fano, F. A., Sukia, M. L., 2011. Intelligent multi-objective nonlinear model predictive control (iMO-NMPC): towards the on-line optimisation of highly complex control problems. Expert systems with applications 39 (7), 6527-6540.
    • Gembicki, F. W., 1974. Vector optimization for control with performance and parameter sensitive indices. Ph.D. thesis, Case Western Reserve University, Cleveland, Ohio.
    • Glover, K., Doyle, J. C., 1988. State-space formulae for all stabilizing controllers that satisfy an h∞ norm bound and relations to risk sensitivity. Systems and Control letters, 167-172.
    • Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
    • Griffin, I. A., Schroder, P., Chipperfield, A. J., Fleming, P. J., 2000. Multi-objective optimization approach to the alstom gasifier problem. Proceedings of the institute of mechanical engineers 214 (1), 453-468.
    • Grosso, P. B., 1985. Computer simulation of genetic adaptation: Parallel subcomponent interaction in a multilocus model. Ph.D. thesis, University of Michigan.
    • Hancock, P. J. B., April 1994. An empirical comparison of selection methods in evolutionary algorithms. In: Fogarty, T. C. (Ed.), Evolutionary Computing - AISB Workshop. Vol. 865 of Lecture Notes in Computer Science. Springer-Verlag, pp. 80-94.
    • Herrero, J. M., Blasco, X., Martinez, M., Sanchis, J., 2008. Multiobjective tuning of robust pid controllers using evolutionary algorithms. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G., Drechsler, R., Ekart, A., Esparcia-Alcazar, A., Farooq, M., Fink, A., McCormack, J., O'Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A., Yang, S. (Eds.), Applications of Evolutionary Computing. Vol. 4974. pp. 515-524.
    • Hiroyasu, T., Miki, M., Watanabe, S., 2000. The new model of parallel genetic algorithm in multi-objective optimization problems - divided range multi-objective genetic algorithm. In: Proceedings of the Congress on Evolutionary Computation (CEC) 2000.
    • Hwang, C.-L., Masud, A. S. M., 1979. Multiple Objective Decision Making - Methods and Applications. Vol. 164 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin.
    • Igel, C., Hansen, N., Roth, S., 2007. Covariance matrix adaptation for multi-objective optimization. Evolutionary Computation 15 (1), 1 - 28.
    • Kleinrock, L., 1975. Queueing Systems Volume 1: Theory. John Wiley & Sons.
    • Klienrock, L., 1969. UCLA press release. URL http://www.lk.cs.ucla.edu/LK/Bib/REPORT/press.html Lee, H.-J., Lee, J.-W., Lee, J.-O., 2009. Development of a web services based multidisciplinary design optimization framework. Advancess in Engineering Software 40 (3), 176-183.
    • McFarlane, D., Glover, K., 1990. Robust Controller Design Using Normalized Coprime Factor Plant Descriptions. Vol. 138 of Lecture Notes in Control and Information Sciences. Springer-Verlag.
    • Michalewicz, Z., Fogel, D. B., 2000. How to Solve It: Modern Heuristics. Springer.
    • Moshaiov, A., Ashram, A., 2009. Multi-objective evolution of robot neuro-controllers. In: IEEE congress on evolutionary computation (CEC) 2009. pp. 1093-1100.
    • Mu¨hlenbein, H., 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, 2nd Edition. McGraw-Hill.
    • Purshouse, R. C., 2003. On the evolutionary optimisation of many objectives. Ph.D. thesis, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK, S1 3JD.
    • Reynoso-Meza, G., Blasco, X., Sanchis, J., Herrero, J. M., 2013. Multiobjective evolutionary algorithms for multivariable pi controller design. Information Sciences 221, 124-141.
    • Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J. M., 2012. Comparison of design concepts in multi-criteria decision-making using level diagrams. Expert Systems with Applications 39 (9), 7895-7907.
    • Rivera, W., 2001. Scalable parallel genetic algorithms. Artificial Intelligence Review 16 (2), 153-168.
    • Rodriguez-Vazquez, K., Fonseca, C. M., Fleming, P. J., 2004. Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming. IEEE transactions on systems, man, and cybenetics - part a: systems and humans 34 (4), 531-545.
    • Schott, J. R., 1995. Fault tolerant design using single and multicriteria genetic algorithm optimization. Master's thesis, Massachusetts Institute of Technology.
    • Shenfield, A., Fleming, P. J., 2013. A novel workload allocation strategy for batch jobs. International Journal of Computing and Network Technology 1, 1-17.
    • Shenfield, A., Fleming, P. J., Alkarouri, M., 2007. Computational steering of a multi-objective evolutionary algorithm for engineering design. Engineering Applications of Artificial Intelligence 20 (8), 1047-1057.
    • Shenfield, A., Fleming, P. J., Allan, J., Kadirkamanathan, V., 2010. Optimisation of maintenance scheduling strategies on the grid. Annals of Operations Research 180 (1), 213 - 231.
    • Skogestad, S., Postlethwaite, I., 1996. Multivariable feedback control - Analysis and Design. John Wiley & Sons.
    • Starkweather, T., Whitley, D., Mathias, K., 1991. Optimization using distributed genetic algorithms. In: Schwefel, H.-P., Ma¨nner, R. (Eds.), Parallel Problem Solving from Nature 1. Vol. 496 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 176-185.
    • Stewart, P., Stone, D. A., Fleming, P. J., 2004. Design of robust fuzzy-logic control systems by multi-objective evolutionary methods with hardware in the loop. Engineering applications of artifical intelligence 70 (3), 275-284.
    • Tan, K. C., Li, Y., 2002. Grey-box model identification via evolutionary computing. Control engineering practice 10, 673-684.
    • The White Rose University Consortium, 2012. The white rose grid website. Viewed 10 January 2012.
    • Van Veldhuizen, D. A., 1999. Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Ph.D. thesis, Airforce Institute of Technology.
    • W3C Working Group, February 2004. Web services architecture. Viewed 18 October 2006.
    • URL http://www.w3c.org/TR/ws-arch
    • Wang, L., Li, L.-P., 2011. Fixed-structure h∞ controller synthesis based on differential evolution with level comparison. Evolutionary Computation, IEEE Transactions on 15 (1), 120-129.
    • Weng, W., Wang, P., Jin, X., Cao, Y., 2012. Design and application of a platform for cae-based optimization using a grid-based environment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 226 (6), 1601-1611.
    • Zames, G., 1981. Feedback and optimal sensitivity: model reference transformations, multiplicitive seminorms, and approximate inverse. IEEE Transactions on Automatic Control 26 (2), 301-320.
    • Zhao, S.-Z., Iruthayarajan, M. W., Suganthan, P. N., 2011. Multi-objective robust pid controller tuning using two lbests multi-objective particle swarm optimisation. Information Sciences 181 (16), 3323-3335.
    • Zitzler, E., Thiele, L., 1998. Multiobjective optimization using evolutionary algorithms a comparative case study. In: Eiben, A. E., B¨ack, T., Schoenauer, M., Schwefel, H. P. (Eds.), Parallel Problem Solving from Nature. Springer, pp. 292-301.
    • Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., da Fonseca, V. G., 2003. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7 (2), 117-132.
    • 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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