Improved Genetic Algorithm with Two-Level Approximation for Truss Optimization by Using Discrete Shape Variables

This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and gene...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-11
Main Authors: Huang, Hai, Li, Dong-fang, Shui, Xiao-fang, Chen, Shen-yan
Format: Article
Language:English
Subjects:
Approximation
Computational efficiency
Continuity (mathematics)
Cost analysis
Efficiency
Engineering
Genetic algorithms
Mathematical analysis
Mathematical models
Optimization
Optimization models
Sensitivity analysis
Shape optimization
Strategy
Structural analysis
Topology
Topology optimization
Trusses
Variables
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Summary:This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and genetic algorithm (GA), a new method for adding shape variables is presented, in which the nodal positions are corresponding to a set of coordinate lists. A uniform optimization model including size/shape/topology variables is established. First, a first-level approximate problem is constructed to transform the original implicit problem to an explicit problem. To solve this explicit problem which involves size/shape/topology variables, GA is used to optimize individuals which include discrete topology variables and shape variables. When calculating the fitness value of each member in the current generation, a second-level approximation method is used to optimize the continuous size variables. With the introduction of shape variables, the original optimization algorithm was improved in individual coding strategy as well as GA execution techniques. Meanwhile, the update strategy of the first-level approximation problem was also improved. The results of numerical examples show that the proposed method is effective in dealing with the three kinds of design variables simultaneously, and the required computational cost for structural analysis is quite small.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/521482