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Method for improving genetic algorithm structural optimization efficiency

A technology for improving genetic algorithm and efficiency, applied in the field of structural optimization efficiency, can solve problems such as high computational complexity and huge storage capacity, and achieve the effects of high computational efficiency, low storage capacity, and improved computational efficiency

Inactive Publication Date: 2009-11-18
WUXI RES INST OF APPLIED TECH TSINGHUA UNIV +1
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Problems solved by technology

But directly storing all the chromosomes that appeared during the evolution process requires a huge amount of storage, which will be unacceptable for large-scale problems
Moreover, direct storage of chromosomes will also result in higher computational complexity for the fitness evaluation of repeated individuals

Method used

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  • Method for improving genetic algorithm structural optimization efficiency
  • Method for improving genetic algorithm structural optimization efficiency
  • Method for improving genetic algorithm structural optimization efficiency

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Embodiment Construction

[0014] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0015] The present invention utilizes the finite element method to carry out structural analysis on the discrete structure, because in the finite element method, the property of the beam unit can express its cross-sectional shape and size, and the material information of this unit, so the present invention adopts the property of the beam unit as the design Variables can realize the comprehensive optimization of cross-sectional shape, size and material. The topology optimization of structures in discrete structure optimization requires the use of topology optimization variables. In order to avoid structural analysis of repetitive individuals, it is necessary to identify and record all non-repetitive individuals that have appeared in the evolution process, and it is necessary to judge whether individuals in the current population have already appeared. ...

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Abstract

The invention relates to a method for improving genetic algorithm structural optimization efficiency, namely a method introducing individual identification codes, comprising the following steps: initializing a species group and an evolutionary history list; calculating the individual identification codes of the current species group; judging whether the individual is an overlapping individual according to the individual identification code; analyzing the structure of the new individual by means of finite element method; updating the evolutionary history list; evaluating the fitness of the overlapping individuals and the new individuals undergoing structural analysis and obtaining corresponding fitness; judging whether the algorithm is finished, if no, obtaining a new species group by carrying out selection, intersection and variation on the species group, and turning to the start step to operate circularly. The invention adopts the individual identification method to solely mark a chromosome with an identification code, thus avoiding the structural analysis of overlapping individuals, decreasing the amount of calculation effectively, improving the calculating efficiency of genetic algorithm structural optimization. The invention can be widely applied to optimization problems in various fields of discrete structures based on genetic algorithm.

Description

technical field [0001] The invention relates to a method for structure optimization efficiency, in particular to a method for improving the efficiency of genetic algorithm structure optimization for discrete structure topology, size, shape and material optimization in the field of structure 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 does not require the objective function and constraint function to be differentiable, easy to deal with discrete problems, and has global convergence ability, so it can solve the difficulty of comprehensive optimization of discrete structures. However, when genetic algorithm is use...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
Inventor 苏瑞意桂良进范子杰
Owner WUXI RES INST OF APPLIED TECH TSINGHUA UNIV
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