摘要
采用改进的遗传算法,求解了具有屈曲约束,尤其截面积是离散型的桁架拓扑优化。本文对离散变量以及拓扑变量分别进行二进制编码、交叉和变异;为适应拓扑优化问题,在优化过程中,对产生的拓扑结构进行检验,有助于最优拓扑的搜索,加速算法的收敛;为增加群体的多样性,对重新开始算子及其终止准则进行改进。改进的遗传算法成功地求解了屈曲约束下、离散截面桁架拓扑优化问题。
We present an improved genetic algorithm （GA） for topology optimization of a truss with discrete sizing and under local buckling constraints. Binary coding is adopted for discrete and topology variables separately. Topology structure is checked in optimization procedure, which is good for searching optimal topology and the convergence of the genetic algorithm. Restart operator is improved to introduce new gene and explore new space. New convergence rule is proposed. Simulation results show that our algorithm is valid and effective.
出处
《机械科学与技术》
CSCD
北大核心
2009年第4期460-463,共4页
Mechanical Science and Technology for Aerospace Engineering
基金
广东工业大学博士启动经费项目(040128)
广东省自然科学基金项目(04105381)
广东省重大技术专项项目(2004A10407002)资助
关键词
屈曲约束
重新开始算子
拓扑检验
遗传算法
buckling constraints
restart operator
topology check
genetic algorithm
