Abstract
In this paper, we suggest a novel problem within the context of multi objective optimization. It concerns the control of solutions’ performances in multi objective spaces. The motivation for controlling these performances comes from an inspiration to improve the robustness of solutions to physical deterioration. When deterioration occurs, the solution performances degrade. In order to prevent extended degradation and loss of robustness, an active control is implemented. Naturally, in order to enable such a control, the solution (product) should have tunable parameters that would serve as the controlled variables. Optimizing the solution for such a problem means that the tunable parameters should be found and their manipulation determined. Here the optimal solutions and the controller are designed using multi and single objective evolutionary algorithms. The paper is concluded with a discussion on the high potential of the approach for research and real life applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mattson, C.A., Messac, A.: Pareto frontier Based concept selection under uncertainty, with visualization. Optimization and Engineering 6, 85–115 (2005)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms, analyzing the State-of-the-Art. Evolutionary Computation 8(2), 12–147 (2000)
Miettinen, K.M.: Non-linear Multiobjective Optimization. International Series on Operations Research and Management Science. Kluwer Academic Publishers, Dordrecht (1999)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Coello, C.A.C.: Recent Trends in Evolutionary Multiobjective Optimization. In: Ajith, A., Jain, L., Robert, G. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, pp. 7–32. Springer, London (2005)
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)
Deb, K.: Multi-objective optimization using evolutionary algorithms. J. Wiley & Sons, Ltd., Chichester (2001)
Phadke, M.S.: Quality engineering using robust design. Prentice-Hall, Englewood Cliffs (1989)
Taguchi, G., Taguchi, S., Tugulum, R.: Taguchi robust engineering: worlds’ best practice for achieving competative advantage in the new millennium. McGraw-Hill, New York (1999)
Branke, J.: Reducing the sampling variance when searching for robust solutions. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 235–242. Morgan Kaufmann Publishers, San Francisco (2001)
Deb, K., Gupta, H.: Searching for robust Pareto-optimal solutions in multi-objective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 150–164. Springer, Heidelberg (2005)
Teich, J.: Pareto Font exploration with uncertain objectives. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 314–328. Springer, Heidelberg (2001)
Luo, L., Kannan, P.K., Besharati, B., Azarm, S.: Design of Robust New Products under Variability: Marketing Meets Design. Journal of Product Innovation Management 22, 177–192 (2005)
Singh, R., Khamba, J.S.: Mathematical modeling of tool wear in ultrasonic machining of titanium. Int. J. Adv. Technol. 4, 573–580 (2009)
Lounis, Z., Madanat, S.M.: Integrating mechanistic and statistical deterioration models for effective bridge management. In: 7th ASCE International Conference on Applications of Advanced Technology in Transportation, Boston, pp. 513–520 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Avigad, G., Eisenstadt, E. (2010). Robustness of Multi-objective Optimal Solutions to Physical Deterioration through Active Control. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-17298-4_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17297-7
Online ISBN: 978-3-642-17298-4
eBook Packages: Computer ScienceComputer Science (R0)