A novel optimization approach for minimum cost design of trusses

• Published in: Computers & structures 2007-12, Vol.85 (23), p.1782-1794
• Main Authors: Kripakaran, Prakash, Gupta, Abhinav, Baugh, John W.
• Format: Article
• Language: English
• Subjects: Computational techniques; Cost minimization; Discrete sizing optimization; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Genetic algorithms; Hybrid search; Mathematical methods in physics; Member grouping; Physics; Solid mechanics; Static elasticity (thermoelasticity...); Structural and continuum mechanics; Structural optimization; Truss design
• ISSN: 0045-7949; 1879-2243
• DOI: 10.1016/j.compstruc.2007.04.006

This paper describes new optimization strategies that offer significant improvements in performance over existing methods for bridge-truss design. In this study, a real-world cost function that consists of costs on the weight of the truss and the number of products in the design is considered. We propose a new sizing approach that involves two algorithms applied in sequence – (1) a novel approach to generate a “good” initial solution and (2) a local search that attempts to generate the optimal solution by starting with the final solution from the previous algorithm. A clustering technique, which identifies members that are likely to have the same product type, is used with cost functions that consider a cost on the number of products. The proposed approach gives solutions that are much lower in cost compared to those generated in a comprehensive study of the same problem using genetic algorithms (GA). Also, the number of evaluations needed to arrive at the optimal solution is an order of magnitude lower than that needed in GAs. Since existing optimization techniques use cost functions like those of minimum-weight truss problems to illustrate their performance, the proposed approach is also applied to the same examples in order to compare its relative performance. The proposed approach is shown to generate solutions of not only better quality but also much more efficiently. To highlight the use of this sizing approach in a broader optimization framework, a simple geometry optimization algorithm that uses the sizing approach is presented. This algorithm is also shown to provide solutions better than the existing results in literature.