A Study of Joint Cost Inclusion in Linear Programming Optimization

P. Armaos

Abstract


The concept of Structural Optimization has been a topic or research over the past century. Linear Programming Optimization has proved being the most reliable method of structural optimization. Global advances in linear programming optimization have been recently powered by University of Sheffield researchers, to include joint cost, self-weight and buckling considerations. A joint cost inclusion scopes to reduce the number of joints existing in an optimized structural solution, transforming it to a practically viable solution. The topic of the current paper is to investigate the effects of joint cost inclusion, as this is currently implemented in the optimization code. An extended literature review on this subject was conducted prior to familiarization with small scale optimization software. Using IntelliFORM software, a structured series of problems were set and analyzed. The joint cost tests examined benchmark problems and their consequent changes in the member topology, as the design domain was expanding. The findings of the analyses were remarkable and are being commented further on. The distinct topologies of solutions created by optimization processes are also recognized. Finally an alternative strategy of penalizing joints is presented.


Keywords


joint cost inclusion; linear programming optimization; structural optimization; IntelliFORM

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References


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D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, Massachusetts, 1989

E. W. Parkes, Joints in optimum frameworks, International Journal of Solids and Structures, Vol. 11, No. 9, pp.1017-1022, 1975




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