Abstract
The problem of computing the hull, that is the tightest interval enclosure of the solution set for linear systems with parameters being nonlinear functions of interval parameters, is an NP-hard problem. However, since the problem of computing the hull can be considered as a combinatorial or as a constrained optimisation problem, metaheuristic techniques might be helpful. Alas, experiments performed so far show that they are time consuming and their performance may depend on the problem size and structure, therefore some acceleration and stabilisation techniques are required. In this paper, a new approach which rely on a multi-agent system is proposed. The idea is to apply evolutionary method and differential evolution for different agents working together to solve constrained optimisation problems. The results obtained for several examples from structural mechanics involving many parameters with large uncertainty ranges show that some synergy effect of the metaheuristics can be achieved, especially for problems of a larger size.
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References
Alefeld, G., Herzberger, J.: Introduction to Interval Computations (transl. by J. Rokne from the original German ‘Einführung In Die Intervallrechnung’), pp. xviii–333. Academic Press Inc., New York (1983)
Alefeld, G., Kreinovich, V., Mayer, G.: The Shape of the Solution Set for Systems of Interval Linear Equations with Dependent Coefficients. Mathematische Nachrichten 192(1), 23–36 (2006)
Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization. Springer (2006)
Duda, J., Skalna, I.: Differential Evolution Applied to Large Scale Parametric Interval Linear Systems. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2011. LNCS, vol. 7116, pp. 206–213. Springer, Heidelberg (2012)
Ferson, S., Ginzburg, L.R.: Different methods are needed to propagate ignorance and variability. Reliability Engineering and Systems Safety 54, 133–144 (1996)
Hanna, L., Cagan, J.: Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization. Journal of Mechanical Design 131(1), 479–487 (2009)
’t Hoen, P.J., de Jong, E.D.: Evolutionary Multi-agent Systems. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 872–881. Springer, Heidelberg (2004)
Moore, R.E.: Interval Analysis. Prentice-Hall, Inc., Englewood Cliffs (1966)
Neumaier, A.: Interval Methods for Systems of Equations, pp. xvi–255. Cambridge University Press, Cambridge (1990)
Popova, E., Iankov, R., Bonev, Z.: Bounding the Response of Mechanical Structures with Uncertainties in all the Parameters. In: Muhannah, R.L., Mullen, R.L. (eds.) Proceedings of the NSF Workshop on Reliable Engineering Computing (REC), pp. 245–265 (2006)
Price, K.S., Rainer, M., Lampinen, J.A.: Differential Evolution. A Practical Approach to Global Optimization. Springer (2005)
Rohn, J., Kreinovich, V.: Computing exact componentwise bounds on solutions of linear systems with interval data is NP-hard. SIAM Journal on Matrix Analysis and Application, SIMAX 16, 415–420 (1995)
Rump, S.M.: Verification Methods for Dense and Sparse Systems of Equations. In: Herzberger, J. (ed.) Topics in Validated Computations, pp. 63–135. Elsevier Science B. V. (1994)
Skalna, I., Duda, J.: A Comparison of Metaheurisitics for the Problem of Solving Parametric Interval Linear Systems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) NMA 2010. LNCS, vol. 6046, pp. 305–312. Springer, Heidelberg (2011)
Talbi, E.G.: Parallel combinatorial optimization. John Wiley and Sons (2006)
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Duda, J., Skalna, I. (2013). Heterogeneous Multi-agent Evolutionary System for Solving Parametric Interval Linear Systems. In: Manninen, P., Öster, P. (eds) Applied Parallel and Scientific Computing. PARA 2012. Lecture Notes in Computer Science, vol 7782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36803-5_35
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DOI: https://doi.org/10.1007/978-3-642-36803-5_35
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