Two dimensional coding and decoding methods of discrete structure genetic optimization
A technology of discrete structure and genetic optimization, which is applied in the field of two-dimensional coding and decoding of discrete structure genetic optimization, can solve the problems that the global optimal solution cannot be found, and the global optimal solution is not in this subset, so as to reduce storage capacity and improve The effect of computing efficiency
Inactive Publication Date: 2011-08-10
WUXI RES INST OF APPLIED TECH TSINGHUA UNIV +1
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However, the topological combination contained in the fixed-length vector encoding is only a subset of all combinations, which may cause the phenomenon that the global optimal solution is not in this subset, resulting in the inability to find the global optimal solution
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[0022] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.
[0023] The present invention utilizes the characteristics of the discrete structure finite element model, adopts the node matrix encoding method, sets the topological value and attribute value of the bar as matrix elements, and adopts the symmetrical sparse matrix method, which can reduce the storage capacity of the encoding and improve the encoding efficiency at the same time. Computational efficiency.
[0024] Such as figure 1 As shown, the steps of the symmetrical sparse node matrix coding method of the present invention are as follows:
[0025] 1) Establish the finite element model of the discrete structure: such as figure 2 As shown, taking the planar six-node truss structure as an example, the finite element model of the planar six-node truss structure is established by using the pre-processing software Msc.Patran. There are six nodes and te...
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The invention relates to two dimensional coding and decoding methods of discrete structure genetic optimization, which set the topology value and the attribute value of a rod piece as matrix elements by utilizing the characteristic of a finite element model of the discrete structure and adopting node matrix equation coding and sparse symmetric matrix methods. The invention is characterized in thatthe coding method comprises the following steps: building the finite element model of the discrete structure; outputting data files needed by a solver; modifying the output data files needed by the solver; reading all the nodes and rod pieces units and the attribute information from the data files needed by the solver and storing in a memory; defining two vacant sparse matrices and defining the dimension as the maximum node number value; assigning the two vacant sparse matrix elements, reading all the unit information and finishing the decoding of the initial structure; traversing all the rodpiece units or not when judging as matrix element assignment; and the decoding method comprises the following steps: acquiring the topology coding matrices of the initial structure and the new individual; calculating the mark matrices of the newly-added or deleted units; updating the attribute coding matrix; numbering all the newly-added rod piece units and updating the rod piece unit matrices; updating data files. The invention can be widely applied to optimization problems in various fields containing discrete structures.
Description
technical field [0001] The invention relates to a genetic algorithm encoding and decoding method, in particular to a two-dimensional encoding and decoding method used for discrete structure genetic optimization in the field of structural optimization. Background technique [0002] Discrete structure optimization can be divided into topology, size, shape and material optimization according to different optimization contents. The comprehensive optimization of discrete structures cannot be realized with traditional optimization algorithms, because this is a discrete, non-convex, multi-variable, multi-domain optimization problem. Genetic algorithm has the ability of global convergence, does not require the objective function and constraint function to be differentiable, and is easy to deal with discrete problems, so it can solve the difficulty of comprehensive optimization of discrete structures. The genetic algorithm is mainly based on Darwin's theory of evolution, Weizmann's ...
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IPC IPC(8): G06N3/12G06F17/50
Inventor 苏瑞意桂良进范子杰
Owner WUXI RES INST OF APPLIED TECH TSINGHUA UNIV