改进的粒子群-模拟退火算法在桁架结构优化设计中的应用

THE APPLICATION OF AN IMPROVED PARTICLE SWARM OPTIMIZATION-SIMULATED ANNEALING ALGORITHM IN TRUSS STRUCTURE OPTIMIZATION DESIGN

由于粒子数目多,维数大,用粒子群算法求解多约束问题的迭代求解过程需耗费大量时间。受达尔文的优胜劣汰自然法则启发,在粒子群算法中引入淘汰择优机制。使算法随着迭代次数增加,适应能力较差的粒子逐步被淘汰。最后留下的最优粒子需要较低的温度进行退火求解,寻得全局最优解。多峰值函数测试表明,改进后的算法能够高效率跳出局部最优寻得全局最优解。将改进的算法用于空间桁架结构优化,经算例表明,改进后的粒子群模拟退火串行算法降低了算法的求解复杂度,具有较好的稳定性和较优的收敛性,适用于空间桁架结构截面尺寸优化设计。

The particle swarm optimization need spend huge time solving tremendous restrain problems due to its large particles and dimensions. Inspired of Darwin＇s superior bad discard by natural law, selecting the superior and eliminating the inferior mechanism is introduced in the particle swarm algorithm. With iteration number increasing, the particles whose adaptability are poor will be phased out. Finally left the optimal particle find global optimal solution which in need lower the temperature of the annealing method. Multimodal function test shows that the improved algorithm can jump out of the local optimum efficiently and search for the global optimal solution. The improved algorithm is used for space truss structure optimization which shows it not can reduce the serial algorithm of solving complexity but has good stability and better convergence. The improved algorithm is suitable for space truss structure section size optimization design.