基于改进的遗传算法的组合网架结构的多参数优化研究

【作者】 韩志刚；

【导师】 肖建春；

【作者基本信息】 贵州大学 ， 结构工程， 2008， 硕士

【摘要】 针对基本遗传算法不适用于组合网架结构多参数优化的特点,对基本遗传算法提出了几点改进措施:①优化的数学模型中引入约束条件;②组合网架结构优化的约束处理方法;③适应度计算公式的修改;④连续设计变量与离散设计变量的混合编码。利用MATLAB语言编程实现了采用改进的遗传算法进行组合网架结构的多参数优化问题,尤其是利用MATLAB语言实现了对基本遗传算法提出的几点改进。本文给出了基于改进的遗传算法的组合网架结构多参数优化的程序,以供设计人员和研究人员参考。选择两个结构优化算例对本文提出的遗传算法的几点改进和编制的优化程序进行验证。第一个算例是平面十杆桁架的杆件截面尺寸的优化,第二个算例是空间网架结构的杆件截面尺寸优化。将两个优化算例的优化结果与其他优化方法的结果进行比较,验证了对遗传算法进行改进后的可靠性及编制的计算机程序的正确性。通过算例对组合网架结构进行几何形状和截面尺寸同时优化的多参数优化,并与其它优化方法进行比较,证明了本文采用的遗传算法的改进措施对组合网架结构进行优化的优越性。更多还原

【Abstract】 This paper proposes a number of improvement measures for multi-parameter optimization of the composite space truss structures according to the characteristic of genetic algorithm.These measures include:①the constraint conditions being introduced to the mathematical model;②the treatment of constraint conditions for composite space truss structure optimization are proposed;③fitness formula is improved based on composite space truss structure optimization;④mixed code for design continuous variables and discrete design variables is used.The MATLAB language to realize the multi-parameter optimization of composite space truss structure is used through improved genetic algorithm.MATLAB language is utilized to realize a number of improvement measures raised by improved genetic algorithm.This paper provides the program of composite space truss structure optimization based on improved genetic algorithm for designers’and researchers’ reference.This paper utilizes structural optimization examples to verify the improved genetic algorithm and the optimization program.The examples are the sectional optimization of the 10-trusses plane truss and the sectional optimization of the space truss structure.Compared with other optimization methods,the improved genetic algorithm and the optimization program are verified to be reliable and correct.By optimizing the geometry shape and the section size of the composite space truss structure at the same time,and comparing with other optimization methods,the improved genetic algorithm is verified to be superior to other optimization methods.更多还原