Improved genetic algorithm for design optimization of truss structures with sizing, shape and topology variables

2005 ◽  
Vol 62 (13) ◽  
pp. 1737-1762 ◽  
Author(s):  
Wenyan Tang ◽  
Liyong Tong ◽  
Yuanxian Gu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Truss Structures ◽  
Improved Genetic Algorithm ◽  
And Topology

Fuzzy-Robust Design Optimization of Dimension Tolerance Using the Improved Genetic Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 1502-1505
Author(s):  
Bo Zhong
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Robust Design ◽  
Treatment Method ◽  
Robust Design Optimization ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Improved Genetic Algorithm ◽  
Design Problems ◽  
Standard Genetic Algorithm

The standard genetic algorithm is improved by introducing the engineering treatment method of design vector in order to solve the optimization problem with mixed-discrete variables. A program of improved genetic algorithm has been designed. It can be used to solve the optimal design problems with continuous variables, discrete variables or mixed-discrete variables. For a dimension chain, the fuzzy-robust design of dimension tolerance is discussed and a model of fuzzy-robust design optimization is established. The solution of established model is achieved by using the improved genetic algorithm and the robustness of the dimension tolerance has been improved. The example shows that the proposed method is effective in engineering design.

scholarly journals A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

2009 ◽  
Vol 2009 ◽  
pp. 1-28 ◽  
Author(s):  
Tugrul Talaslioglu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Truss Structures ◽  
Single Population ◽  
Multiple Populations ◽  
Promising Solution ◽  
Feasible Solutions

A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

Improved genetic algorithm for mixed-discrete-continuous design optimization problems

2010 ◽  
Vol 42 (10) ◽  
pp. 927-941 ◽  
Author(s):  
Kuo-Ming Lee ◽  
Jinn-Tsong Tsai ◽  
Tung-Kuan Liu ◽  
Jyh-Horng Chou
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Optimization Problems ◽  
Improved Genetic Algorithm

scholarly journals Sizing and Topology Optimization of Trusses Using Genetic Algorithm

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 715
Author(s):  
Ingrid Delyová ◽  
Peter Frankovský ◽  
Jozef Bocko ◽  
Peter Trebuňa ◽  
Jozef Živčák ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Point Of View ◽  
Truss Structures ◽  
And Topology ◽  
Design Variables ◽  
Structural Engineer ◽  
Topology Optimization Of Trusses ◽  
Structural Responses ◽  
Genetic Algorithm Approach

Genetic algorithms are a robust method for a solution of wide variety optimization problems. It explores a big space of design variables in order to find the best solution. From the point of view of a user, the algorithm requires the encoding of design variables into the form of strings and the procedure of optimization uses them for optimization. Here, for the structural engineer, it is crucial to find the form of objective function including the constraints of the task and also to avoid critical states during the solution of structural responses. This paper presents the use of genetic algorithm for solving truss structures. The use of genetic algorithm approach is shown on three cases of truss structures.

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