模拟植物生长算法(PGSA)是以植物向光性机理(形态素浓度理论)为启发准则的智能优化新算法，具有高效的搜索能力。粒子群算法(PSO)是一种来源于鸟群觅食的启发式优化算法，具有算法规则简单、高鲁棒性等特点。本文以模拟植物生长算法为基础，对其基本原理进行分析，指出了不同初始生长点的选取会影响该优化算法能否收敛于全局最优解。为此，提出了新的混合策略(PGSA-PSO混合智能优化算法)：先基于粒子群算法的高鲁棒性初选优秀的初始生长点，再基于模拟植物生长算法的高效搜索能力以得到最终的全局最优解。通过算例验证了该混合策略可有效提高模拟植物生长算法的全局搜索能力。最后，利用该混合策略对典型的弦支穹顶结构预应力优化问题进行分析，结果表明此混合智能优化算法的优化效果显著，在结构优化问题中具有较好的可行性和有效性。

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.