基于双生长点并行生长机制的模拟植物生长算法及其结构优化

Plant growth simulation algorithm based on double growth point parallel growth mechanism and its structural optimization

针对模拟植物生长算法(PGSA)系列算法中存在的搜索路径相对单一、搜索覆盖面不够广等问题,结合复杂结构优化问题中设计变量多、存在多个局部最优解、算法难以自动终止等特点,基于PGSA的基本原理和植物的实际生长规律,提出一种新的算法机制——双生长点并行生长机制,并与基于生长空间限定与并行搜索(GSL&PS-PGSA)算法相融合。通过典型数学及空间桁架结构算例进行了验证,结果表明:双生长点并行生长机制增加了寻优搜索路径,拓宽了搜索覆盖面,降低了陷入局部最优解的概率,并为算法提供更为有效的终止机制,从而具有更加显著的优化效率及全局搜索能力;与序列两级算法、蚁群算法等常用优化方法相比,融入双生长点并行生长机制的GSL&PS-PGSA进一步提升了算法的优化求解能力,在结构优化问题中表现出良好的适应性及有效性。

Plant growth simulation algorithm(PGSA) and its improved algorithms still have the defects of relatively single search path, insufficient search coverage and so on. The complex structural optimization problems have the characteristics of multiple design variables, multiple local optimal solutions, difficulty in automatic terminate and so on. In order to address the above defects and characteristics, a new algorithm mechanism(double growth point parallel growth mechanism) is proposed based on the basic principle of PGSA and the actual growth law of plants. This new mechanism is also integrated with GSL&PS-PGSA(growth space limitation & parallel search) algorithm. The effectiveness of the proposed mechanism is verified by typical mathematic examples and structural example of space truss. The results show that the double growth point parallel growth mechanism can increase search paths, widen search area, and reduce the probability of falling into local optimal solution. It provides a more effective termination mechanism for the algorithm, and thus encourages more significant optimization efficiency and global search ability of the algorithm. Compared with other commonly used optimization methods such as Sequential two-level algorithm and Ant colony algorithm, the GSL&PS-PGSA with the double growth point parallel growth mechanism further improves the optimization ability. It shows fine adaptability and effectiveness in structural optimization problems.