Multi-database exploration of large design spaces in the framework of cascade evolutionary structural sizing optimization

• Published in: Computer methods in applied mechanics and engineering Vol. 194; no. 30; pp. 3315 - 3330
• Main Authors: Charmpis, Dimos C., Lagaros, Nikolaos D., Papadrakakis, Manolis
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2005
• Subjects: Cascade; Design space; Evolutionary algorithms; Sizing optimization; Design space; Database; Evolutionary algorithms; Cascade; Sizing optimization
• ISSN: 0045-7825; 1879-2138
• DOI: 10.1016/j.cma.2004.12.020

In discrete sizing optimization of truss and frame structures the design variables take values from databases, which are usually populated with a relatively small number of cross-section types and sizes. The aim of this work is to allow the use of large-size databases in discrete structural sizing optimization problems, in order to enrich the set of design variable options and increase the potential of achieving high-quality optimal designs. For this purpose, the concept of coarse database is introduced, according to which smaller-size versions of an appropriately ordered large database can be constructed. This concept is combined with the idea of cascading, which allows a single optimization problem to be tackled with a number of autonomous optimization stages. Under this context, several coarse versions of the same full-size database are formed, in order to utilize a different database in each cascade stage executed with an evolutionary optimization algorithm. The first optimization stages of the resulting multi-database cascade procedure make use of the coarsest database versions available and serve the purpose of basic design space exploration. The last stages exploit finer databases (including the original full-size database) and aim in fine tuning the achieved optimal solution. Based on the reported numerical results, multi-database cascading proves to be an effective tool for the handling of large databases and corresponding extensive design spaces in the framework of discrete structural sizing optimization applications.