考虑抗震性约束的建筑空间结构优化

当前对建筑空间结构进行优化时,所采用的算法趋同性高,无法实现多目标种群优化,易陷入局部最优解,存在寻优质量低、优化成本高、抗震性能低的问题。针对上述问题,提出一种基于改进粒子群算法的建筑空间结构优化方法。该方法以空间结构的抗震性能、工程造价为优化目标,来优化建立建筑空间结构设计;引入多子群协同进化机制解决建筑空间结构抗震优化设计中多目标间的种群优化问题,同时引入外部档案和精英学习策略改进粒子群算法,筛选出满足目标函数的最优设计方案,完成抗震性约束的建筑空间结构优化。实验结果表明:所提方法对建筑空间结构优化时的特点为寻优质量高、优化成本低、抗震性能高。

Optimization of Spatial Building Structures Considering Seismic Constraints

In the current method of optimizing the design of spatial building structures, the high convergence of the algorithm makes it impossible to realize multi-objective population optimization, and it is easy to fall into the local optimal solution. The method has the problems of low optimization quality, high optimization cost, and low seismic performance. To solve the above problems, in this work, a method of optimizing the design of spatial building structures based on the improved particle swarm algorithm is proposed, where the seismic performance and engineering cost of space structure are the optimization target. A co-evolutionary multiple sub-groups mechanism was introduced to solve the problem of population optimization among multiple objectives in the optimization of the seismic design of spatial structures. Meanwhile, the elite learning strategy was introduced to improve the particle swarm algorithm. The optimal design scheme satisfying the objective function was selected, and the optimization of the building space structure with seismic constraints was completed. The experimental results showed that the proposed method is characterized by high optimization quality, low optimization cost, and high seismic performance.