Elasto-plastic analysis based truss optimization using Genetic Algorithm

2013 ◽  
Vol 50 ◽  
pp. 1-12 ◽  
Author(s):  
Huaguo Wang ◽  
Hiroshi Ohmori
Keyword(s):  
Genetic Algorithm ◽  
Truss Optimization ◽  
Plastic Analysis

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

scholarly journals Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable

2018 ◽  
Vol 5 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Akshay Kumar ◽  
H K Rangavittal
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Global Optimum ◽  
Research Field ◽  
Optimization Techniques ◽  
Truss Optimization ◽  
Function Evaluation ◽  
Selection Function ◽  
Nonlinear Constraint ◽  
Discrete Area

The Genetic Algorithm is one of the advanced optimization techniques frequently used for solving complex problems in the research field, and there are plenty of parameters which affect the outcome of the GA. In this study, a 25-bar truss with the nonlinear constraint is chosen with the objective to minimize the mass and variables being the discrete area. For the same, GA parameter like Selection Function, Population Size, Crossover Function, and Creation Function are varied to find the best combination with minimum function evaluation. It is found that the Uniform selection gives the best result irrespective of the creation function, population size or crossover functions. But this is at the cost of a large number of function evaluations, and the other selection function fails to reach the global optimum and has a smaller number of function evaluation count. If the analysis of selection function is done one at a time, it is seen that all Cases performs better in Roulette but, Case A which is non-integer type with 200 population size being computationally cheaper than Case B and C of population size 300. In the Tournament selection, Case A, B with smaller population size and Case C with higher population size performs better. Case C performs better at Remainder selection with smaller population size, and Case A and B for Stochastic Uniform with higher population size. And, it is clear that the function evaluation count increases with the population size in every Case from this study.

Micro-scale truss optimization using genetic algorithm

2010 ◽  
Vol 43 (5) ◽  
pp. 647-656 ◽  
Author(s):  
María Belén Prendes-Gero ◽  
Jean-Marc Drouet
Keyword(s):  
Genetic Algorithm ◽  
Truss Optimization ◽  
Micro Scale

An adaptive reanalysis method for genetic algorithm with application to fast truss optimization

2009 ◽  
Vol 26 (2) ◽  
pp. 225-234 ◽  
Author(s):  
Tao Xu ◽  
Wenjie Zuo ◽  
Tianshuang Xu ◽  
Guangcai Song ◽  
Ruichuan Li
Keyword(s):  
Genetic Algorithm ◽  
Truss Optimization

scholarly journals Optimization of Support Structures for Offshore Wind Turbines Using Genetic Algorithm with Domain-Trimming

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Mohammad AlHamaydeh ◽  
Samer Barakat ◽  
Omar Nasif
Keyword(s):  
Genetic Algorithm ◽  
High Performance ◽  
Optimization Technique ◽  
Three Dimensional ◽  
Offshore Wind ◽  
Truss Optimization ◽  
Offshore Wind Turbine ◽  
Benchmark Problem ◽  
Algorithm Optimization ◽  
Cross Sectional

The powerful genetic algorithm optimization technique is augmented with an innovative “domain-trimming” modification. The resulting adaptive, high-performance technique is called Genetic Algorithm with Domain-Trimming (GADT). As a proof of concept, the GADT is applied to a widely used benchmark problem. The 10-dimensional truss optimization benchmark problem has well documented global and local minima. The GADT is shown to outperform several published solutions. Subsequently, the GADT is deployed onto three-dimensional structural design optimization for offshore wind turbine supporting structures. The design problem involves complex least-weight topology as well as member size optimizations. The GADT is applied to two popular design alternatives: tripod and quadropod jackets. The two versions of the optimization problem are nonlinearly constrained where the objective function is the material weight of the supporting truss. The considered design variables are the truss members end node coordinates, as well as the cross-sectional areas of the truss members, whereas the constraints are the maximum stresses in members and the maximum displacements of the nodes. These constraints are managed via dynamically modified, nonstationary penalty functions. The structures are subject to gravity, wind, wave, and earthquake loading conditions. The results show that the GADT method is superior in finding best discovered optimal solutions.

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