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Large scale structural optimization: Computational methods and optimization algorithms

M. Papadrakakis, Nikolaos D. Lagaros, Y. Tsompanakis, V. Plevris

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Summary

The objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programmingand evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particularemphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimizationprocedure. Modified versions of both genetic algorithms and evolution strategies combined with mathematical programming methodsto form hybrid methodologies are also tested and compared and proved particularly promising. Furthermore, the structural analysisphase is replaced by a neural network prediction for the computation of the necessary data required by the evolutionary algorithms.Advanced domain decomposition techniques particularly tailored for parallel solution of large-scale sensitivity analysis problemsare also implemented. The efficiency of a rigorous approach for treating seismic loading is investigated and compared witha simplified dynamic analysis adopted by seismic codes in the framework of finding the optimum design of structures with minimumweight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. Theseaccelerograms constitute the multiple loading conditions under which the structures are optimally designed. The numericaltests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scaleoptimization problems.

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