作为一种新型启发式智能优化算法，模拟植物生长算法(PGSA)建立以植物向光 性机理为基础的生长动力模型，以形成向全局最优解迅速生长的搜索机制． 针对大规模复 杂优化问题中生长空间大、设计变量多、可能存在多个局部最优解、算法难以自动终止等 特点，基于 PGSA 基本原理，提出了 3 种新的算法改进机制———可生长点集合限定机制、 新增可生长点剔除机制以及混合步长并行搜索机制，并通过典型数学和桁架结构算例分 析对提出的改进算法的效果进行验证． 结果表明:可生长点集合限定机制能有效控制生长 空间规模，具有较强的局部搜索能力;新增可生长点剔除机制通过与前者的结合，为 PGSA 提供了有效的算法终止机制;混合步长并行搜索机制在生长前期便具备优异的全局搜索 能力，能快速获取到最优解范围． 所提出的新机制显著提升了 PGSA 算法优化的有效性及 适应性，从而为结构优化问题提供了新思路．

As a new heuristic intelligent optimization algorithm，plant growth simulation algorithm (PGSA) esta- blishes the dynamic growth model based on the plant phototropism mechanism and forms the search mechanism rapid- ly towards the global optimal solution． According to the characteristics of large-scale complex optimization problems such as large growth space，multiple design variables，multiple local optimal solutions，difficulty in automatic ter- mination and so on，three novel improved mechanisms (the limited strategy of growth point set，the elimination strategy of new growth points，the parallel search strategy of mixed step size) were proposed based on the basic principle of PGSA and the effectiveness of the proposed improved mechanisms were proved by typical mathematic example and structural example of truss． Several conclusions can be drawn: (1) the scale of growth space can be effectively controlled by the limited strategy of growth point set，which leads to high search capacity of the algo- rithm; (2) combined with the former strategy，the elimination strategy of new growth points can provide effective termination mechanism for PGSA; (3) excellent global search capacity can be offered by the parallel search strate- gy of mixed step size in the early growth stage and the optimal solution range can be quickly obtained． The pro- posed mechanisms can dramatically improve the effectiveness and adaptability of PGSA in optimization，which pro- vides a new approach for structural optimization problems．