关键词: 模拟植物生长算法, 粒子群算法, 混合策略, 结构优化, 弦支穹顶结构

Abstract: Plant Growth Simulation Algorithm (PGSA) is a new intelligent algorithm based on the plant phototropism mechanism——morphactin concentration theory, which has efficient searching ability. Particle Swarm Optimization (PSO) is a heuristic optimization algorithm derived from bird swarm foraging, which has the advantages of simple rules and high robustness. By analyzing the basic principle of PGSA, it is pointed out that the selection of different initial growth points will affect whether PGSA can converge to the global optimal solution. Therefore, a new mixed strategy (PGSA-PSO mixed intelligent optimization algorithm) is proposed. First, the excellent initial growth points are selected based on the high robustness of PSO. Then based on the efficient search ability of PGSA, the final global optimal solution is obtained. A numerical example is given to verify that PGSA-PSO effectively improves the global search ability of PGSA. Furthermore, PGSA-PSO is used to analyze the typical prestressing optimization problem of suspended dome structures. The result shows that PGSA-PSO has a better optimization effect. Consequently, it has good feasibility and effectiveness in structural optimization problem.

Key words: plant growth simulation algorithm, particle swarm optimization, mixed strategy, structural prestress optimization, suspended dome structures