Cascade Structural Sizing Optimization with Large Numbers of Design Variables

• Published in: CivilEng Vol. 3; no. 3; pp. 717 - 733
• Main Authors: Charmpis, Dimos C., Lagaros, Nikos D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Aerospace engineering; cascade optimization; Civil engineering; Configuration management; Design engineering; Design optimization; discrete design space; discrete optimization; Mathematical programming; Optimization algorithms; real-world problems; Sizing; structural optimization; Variables
• ISSN: 2673-4109; 2673-4109
• DOI: 10.3390/civileng3030041

In structural sizing optimization problems, the number of design variables typically used is relatively small. The aim of this work is to facilitate the use of large numbers of design variables in such problems, in order to enrich the set of available design options and offer the potential of achieving lower-cost optimal designs. For this purpose, the concept of cascading is employed, which allows an optimization problem to be tackled in a number of successive autonomous optimization stages. In this context, several design variable configurations are constructed, in order to utilize a different configuration at each cascade sizing optimization stage. Each new cascade stage is coupled with the previous one by initializing the new stage using the finally attained optimum design of the previous one. The first optimization stages of the cascade procedure make use of the coarsest configurations with small numbers of design variables and serve the purpose of basic design space exploration. The last stages exploit finer configurations with larger numbers of design variables and aim at fine-tuning the achieved optimal solution. The effectiveness of this sizing optimization approach is assessed using real-world aerospace and civil engineering design problems. Based on the numerical results reported herein, the proposed cascade optimization approach proves to be an effective tool for handling large numbers of design variables and the corresponding extensive design spaces in the framework of structural sizing optimization applications.