Finding evenly spaced fronts for multiobjective control via averaging Hausdorff-measure

CCE 2011 - 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, Program and Abstract Book

Volumn , Issue , 2011, Pages

  Gerstl, K ^a   Rudolph, G ^a   Schutze O ^b   Trautmann, H ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

^b DEPARTAMENTO DE FÍSICA   (Mexico)

Author keywords

averaged Hausdorff distance; dominated hypervolume; multiobjective control; SMS EMOA

Indexed keywords

APPROXIMATION QUALITY; BENCH-MARK PROBLEMS; HAUSDORFF DISTANCE; HYPERVOLUME; MULTI OBJECTIVE ALGORITHM; MULTI-OBJECTIVE CONTROL; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; NOVEL ALGORITHM; NSGA-II; PARETO FRONT; PERFORMANCE INDICATORS; SELECTION MECHANISM; SMS-EMOA; SOLUTION SET; STOCHASTIC SEARCH ALGORITHMS;

AUTOMATION; BENCHMARKING; CONTROL; ELECTRICAL ENGINEERING; GEOMETRY; MULTIOBJECTIVE OPTIMIZATION; PROBLEM SOLVING; PROCESS CONTROL; TEACHING;

EVOLUTIONARY ALGORITHMS;

EID: 84855799148     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICEEE.2011.6106656     Document Type: Conference Paper

References (21)

1. E. Zitzler and L. Thiele. Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In Conference on Parallel Problem Solving from Nature (PPSN V), pages 292-301, Amsterdam, 1998.
2. D. A. Van Veldhuizen. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999.
3. C. A. Coello Coello and N. Cruz Cortés. Solving Multiobjective Optimization Problems using an Artificial Immune System. Genetic Programming and Evolvable Machines, 6(2):163-190, June 2005.
4. J. Knowles and D. Corne. On metrics for comparing nondominated sets. In Congress on Evolutionary Computation (CEC'2002), volume 1, pages 711-716, Piscataway, New Jersey, May 2002. IEEE Service Center.
5. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. Grunert da Fonseca. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation, 7(2):117-132, 2003.
6. G.P. Liu, J.B. Yang, and J.F. Whidborne. Multiobjective Optimisation and Control. Research Studies Press Ltd., Baldock (UK), 2003.
7. A. Pottharst, K. Baptist, O. Schütze, J. Böcker, N. Fröhlecke, and M. Dellnitz. Operating point assignment of a linear motor driven vehicle using multiobjective optimization methods. Proceedings of the 11th International Conference EPE-PEMC 2004, Riga, Latvia., 2004.
8. K. Witting, B. Schulz, A. Pottharst, M. Dellnitz, J. Böcker, and N. Fröhleke. A new approach for online multiobjective optimization of mechatronical systems. International Journal on Software Tools for Technology Transfer, 10:223-231, 2008.
9. N. Beume, B. Naujoks, and M. Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653-1669, 2007.
10. K. Deb, A. Pratap, Sameer Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002.
11. O. Schütze, X. Esquivel, A. Lara, and C. A. Coello Coello. Using the averaged hausdorff distance as a performance measure in evolutionary multi-objective optimization. To appear in IEEE Transactions on Evolutionary Computation, see also http://delta.cs.cinvestav.mx/~schuetze/technical reports/index.html, 2007.
12. K. Bringmann and T. Friedrich. Tight bounds for the approximation ratio of the hypervolume indicator. In R. Schaefer et al., editors, Proc. of 11th Int'l. Conf. on Parallel Problem Solving from Nature (PPSN XI), pages 607-616, Berlin, 2010. Springer.
13. K. Bringmann and T. Friedrich. The maximum hypervolume set yields near-optimal approximation. In Proc. of 12th Int'l. Conf. on Genetic and Evolutionary Computation (GECCO 2010), pages 511-518. ACM Press, 2010.
14. T. Wagner, N. Beume, and B. Naujoks. Pareto-, aggregation-, and indicator-based methods in many-objective optimization. In S. Obayashi et al., editors, Proc. Evolutionary Multi-CriterionOptimization, 4th Int'l Conf. (EMO 2007), pages 742-756,Berlin, 2007. Springer.
15. J. Mehnen, T.Wagner, and G. Rudolph. Evolutionary optimization of dynamic multi-objective test functions. In Proceedingsof the Second Italian Workshop on Evolutionary Computation(GSICE2), 2006. published on CD-ROM.
16. H. Heuser. Lehrbuch der Analysis, Teil 2. Teubner, Stuttgart, 13th edition, 2004.
17. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable multiobjective optimization test problems. In Proc. of Congress on Evolutionary Computation (CEC 2002), pages 825-830, Piscataway, 2002. IEEE Press.
18. O. Schütze, M. Laumanns, E. Tantar, C. A. Coello Coello, and E.-G. Talbi. Computing gap free Pareto front approximationswith stochastic search algorithms. Evolutionary Computation, 18(1):65-96, 2010.
19. E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. EvolutionaryComputation, 8(2):173-195, 2000.
20. K. Gerstl. Gleichmäßige Paretofront-Approximationen für mehrkriterielle Kontrollprobleme unter Verwendung des gemittelten Hausdorff-Maßes. Master's thesis, Department of Computer Science, TUDortm und University, August 2011.
21. J.J. Durillo and A.J. Nebro. jMetal: A Java framework for multiobjective optimization. Advances in Engineering Software, 42:760-771, 2011.