Abstract
Modified Augmented Lagrangian Genetic Algorithm (ALGA) and Quadratic Penalty Function Genetic Algorithm (QPGA) optimization methods are proposed to obtain truss structures with minimum structural weight using both continuous and discrete design variables. To achieve robust solutions, Compressed Sparse Row (CSR) with reordering of Cholesky factorization and Moore Penrose Pseudoinverse are used in case of non-singular and singular stiffness matrix, respectively. The efficiency of the proposed nonlinear optimization methods is demonstrated on several practical examples. The results obtained from the Pratt truss bridge show that the optimum design solution using discrete parameters is 21% lighter than the traditional design with uniform cross sections. Similarly, the results obtained from the 57-bar planar tower truss indicate that the proposed design method using continuous and discrete design parameters can be up to 29% and 9% lighter than traditional design solutions, respectively. Through sensitivity analysis, it is shown that the proposed methodology is robust and leads to significant improvements in convergence rates, which should prove useful in large-scale applications.
Highlights
IntroductionJournal of Civil Engineering and Management, 2017, 23(2): 252–262 kinematic stability constraints
Structural optimization techniques are effective tools that can be used to obtain lightweight, low-cost and high performance structures
This paper aims to develop an efficient hybrid Genetic algorithms (GAs) method for size and topology optimization of truss structures using both continuous and discrete design variables
Summary
Journal of Civil Engineering and Management, 2017, 23(2): 252–262 kinematic stability constraints. Dede et al (2011) combined GA with value and binary encoding for continuous and discrete optimization of trusses to minimize structural weight based on stress and displacement constraints. The results of their study indicated that multiple truss topologies with almost equal overall weight can be found concurrently as the number of members in the ground structure increases. They showed that the value encoding method requires less computer memory and computational time to achieve optimum solutions. This paper aims to develop an efficient hybrid GA method for size and topology optimization of truss structures using both continuous and discrete design variables. The efficiency of the proposed methods to obtain reliable optimum solutions is investigated through sensitivity analysis
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