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群搜索(GSO)算法是一种新的群智能优化算法,也是一种随机优化算法,适用于结构优化设计,目前在空间结构的优化设计应用中,主要用于铰接结构体系,可以用于连续变量的结构设计,也可用于离散变量的结构优化。基于GSO算法流程,研究了其应用于刚接结构体系的可行性及有效性,编写了框架结构离散变量优化设计相关计算程序,对两个框架结构进行了截面优化计算分析,并用通用有限元软件对其优化结果进行了校核,与已有文献优化结果进行了比较。研究表明:GSO优化算法相对于改进的粒子群算法(HPSO)和其它进化算法都有较好的收敛精度和收敛速度,同时GSO算法较其它算法简单,容易实现,其特殊的搜索模式可以避免大量不必要的结构重分析,节省大量的计算时间,特别适用于复杂工程结构的优化设计及应用。
The group search (GSO) algorithm is a new swarm intelligence optimization algorithm, which is also a stochastic optimization algorithm, which is suitable for structural optimization design. Currently, it is mainly used in articulated structural systems in the optimization design of space structure, and can be used in continuous The structural design of variables can also be used to optimize the structure of discrete variables. Based on the flow of GSO algorithm, the feasibility and effectiveness of the GSO system applied to the rigid-frame structure system are studied. The calculation program of discrete variable optimization design for the frame structure is compiled, and the cross-section optimization calculation and analysis of the two frame structures are carried out. The common finite element software The optimization results were checked and compared with the existing literature optimization results. The results show that the GSO algorithm has better convergence precision and convergence speed than the HPSO and other evolutionary algorithms, and the GSO algorithm is simpler and easier to implement than other algorithms. The special search mode can avoid a large number of problems Unnecessary structural reanalysis, saving a lot of computing time, especially for the optimal design and application of complex engineering structures.