基于生长空间限定与并行搜索的模拟植物生长算法的空间结构优化方法

为弥补传统设计理念和优化方法的不足，促进空间结构的发展与创新，结合前沿优化理论，依据模拟植物生长算法(PGSA)的基本原理，提出基于生长空间限定与并行搜索的模拟植物生长算法(GSL&PS-PGSA)，并与空间结构优化相结合，建立了基于GSL&PS-PGSA的空间结构优化方法。给出了相应的结构优化流程，并采用MATLAB及ANSYS二次开发语言APDL编制了优化程序。通过单层球面网壳截面优化和弦支穹顶预应力优化的典型空间结构算例分析，结果表明：所提出的GSL&PS-PGSA为算法提供了有效的终止机制，且具有高效的计算效率及全局搜索能力；与遗传算法(GA)、粒子群算法(PSO)、ANSYS自带优化方法以及其他改进PGSA算法等相比，GSL&PS-PGSA的优化效果更为显著且具有明显优势；所建立的基于GSL&PS-PGSA的空间结构优化方法，可适用于各类传统和新型空间结构体系的优化问题。

Optimization method of spatial structures based on growth space limited¶llel search-based plant growth simulation algorithm

In order to overcome the shortcomings of traditional design concepts and optimization methods, and to promote the development and innovation of spatial structures, a new plant growth simulation algorithm (PGSA) based on growth spatial limitation and parallel search (GSL&PS-PGSA) is proposed, which combines the frontier optimization theory with the basic principle of PGSA. An optimization method of spatial structures is then established based on GSL&PS-PGSA. The corresponding structural optimization process is given, and the optimization programs are compiled by MATLAB as well as the secondary development language APDL of ANSYS. The section optimization of typical single-layer spherical latticed shells and the prestress optimization of typical suspended domes are analyzed. The results show that the proposed algorithm provides an effective termination mechanism for the algorithm, and has high calculation efficiency and a global search capacity. Compared with the genetic algorithm (GA), particle swarm optimization (PSO), the embedded optimization method in ANSYS and other improved PGSA methods, GSL&PS-PGSA has more significant optimization effects, and its optimization results have obvious advantages. The proposed optimization method of spatial structures based on GSL&PS-PGSA can be applied to the optimization problems of various traditional and new spatial structure systems.