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Research on the optimisation of complex models of large-scale building structures dependent on adaptive grey genetic algorithms

机译：基于自适应灰色遗传算法的大型建筑结构复杂模型优化研究

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Genetic algorithm (GA) is a bionics algorithm based on the biological evolution theory that has received extensive attention in the field of computer science and optimisation in recent years. This paper analyses and integrates the relevant contents of genetic algorithm and its application in the optimal design of large-scale building structures and analyses and researches briefly several key factors when the genetic algorithm is applied to the optimal design of large-scale building structures, such as mathematical modelling, constraint condition treatment, generation of initial population and selection of control parameters of genetic algorithm. However, because the simple genetic algorithm is only good at global search, and the local search ability is not enough, it will take quite a long time to achieve the real optimal solution. For the shortcomings of simple genetic algorithm, an improved adaptive grey genetic algorithm is proposed in this paper. The example shows that the obtained adaptive genetic algorithm can improve the convergence and calculation speed when the genetic algorithms is applied to structural optimisation design.
机译：遗传算法（GA）是一种基于生物演进理论的仿生算法，近年来在计算机科学与优化领域得到了广泛的关注。本文分析并集成了遗传算法的相关内容及其在大型建筑结构的最优设计中的应用，并在遗传算法应用于大型建筑结构的最优设计时，简要介绍了几个关键因素的研究作为数学建模，约束条件处理，初始群体的产生和遗传算法的控制参数选择。但是，由于简单的遗传算法仅擅长全球搜索，而本地搜索能力是不够的，所以实现真正的最佳解决方案需要很长时间。对于简单遗传算法的缺点，本文提出了一种改进的自适应灰色遗传算法。该示例显示，当遗传算法应用于结构优化设计时，所获得的自适应遗传算法可以提高收敛和计算速度。

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