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

Xiaohong Shi
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引用次数: 0

摘要

遗传算法是一种基于生物进化理论的仿生学算法,近年来在计算机科学和优化领域受到广泛关注。本文对遗传算法的相关内容及其在大型建筑结构优化设计中的应用进行了分析和整合,并对遗传算法应用于大型建筑结构优化设计时的数学建模、约束条件处理、初始种群的生成和控制参数的选择等几个关键因素进行了简要分析和研究。但是,由于简单的遗传算法只擅长全局搜索,局部搜索能力不够,要达到真正的最优解需要相当长的时间。针对简单遗传算法的不足,提出了一种改进的自适应灰色遗传算法。算例表明,将所得到的自适应遗传算法应用于结构优化设计时,可以提高遗传算法的收敛性和计算速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the optimisation of complex models of large-scale building structures dependent on adaptive grey genetic algorithms
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.
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