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Efficient Parallel Nash Genetic Algorithm for Solving Inverse Problems in Structural Engineering

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Mathematical Modeling and Optimization of Complex Structures

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 40))

Abstract

A parallel implementation of a game-theory based Nash Genetic Algorithm (Nash-GAs) is presented in this paper for solving reconstruction inverse problems in structural engineering. We compare it with the standard panmictic genetic algorithm in a HPC environment with up to eight processors. The procedure performance is evaluated on a fifty-five bar sized test case of discrete real cross-section types structural frame. Numerical results obtained on this application show a significant achieved increase of performance using the parallel Nash-GAs approach compared to the standard GAs or Parallel GAs.

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Acknowledgments

This research work is funded through contract CAS12/00400 by Ministerio de Educación, Cultura y Deporte of the Government of Spain, through the Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I+D+I 2008-2011 “José Castillejo”, extended by agreement of Consejo de Ministros of 7th October 2011. The second author gratefully acknowledges support at the Mathematical Information Technology Department, University of Jyväskylä (Finland) given by, in particular, Prof. Pekka Neittaanmäki.

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Correspondence to Jacques Périaux .

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Appendix

Appendix

See Tables 7, 8 and 9.

Table 7 Design of reference in inverse problem design (cross-section type detail)
Table 8 Search space of variables (beams 1–25 and columns 26–55)
Table 9 Stresses (\(\text{ MPa } \times 10^{-1}\)) of reference in inverse problem design

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Périaux, J., Greiner, D. (2016). Efficient Parallel Nash Genetic Algorithm for Solving Inverse Problems in Structural Engineering. In: Neittaanmäki, P., Repin, S., Tuovinen, T. (eds) Mathematical Modeling and Optimization of Complex Structures. Computational Methods in Applied Sciences, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-23564-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-23564-6_13

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