基于改进双种群遗传算法的复合材料层合板铺层优化设计

STACKING SEQUENCE OPTIMIZATION OF COMPOSITE LAMINATE BASED ON IMPROVED DUAL POPULATION GENETIC ALGORITHM

针对复合材料层合板铺层优化设计过程中易出现的算法早熟及收敛慢的问题,结合多种群遗传算法和自适应遗传算法,提出一种引入自适应算子的改进双种群遗传算法。在算法过程中以对称复合材料层合板为例进行验证,通过Patran建立有限元初始模型,采用Matlab编写遗传算法主程序及数据传递程序,实现对Nastran的输入输出文件的读写,并在以Tsai-Wu准则为基础确立的适应度函数下,对复合材料层合板的铺层顺序进行优化。对比传统遗传算法,结果表明该改进算法能够明显提高优化效率,并能够有效收敛于全局最优解,对解决复合材料结构优化设计问题有一定的指导意义。

In order to solve the problem of premature convergence or slow convergence of algorithms, which can be easily seen in the progress of composite laminates design and optimization, through combining multi population genetic algorithm and adaptive genetic algorithm, a new improved dual population genetic algorithm with a adaptive operator is proposed. Taking as an example of symmetric laminated for test, a Patran finite element initial model is established. And, with Matlab, the main program of genetic algorithm and data transfer procedures is compiled, by which the Nastran input and output files can be written and read. Then a fitness function based on Tsai-Wu failure criterion is established, and the laminate stacking sequence is optimized. Compared with the traditional genetic algo- rithm, the results show that the improved algorithm can improve the optimization efficiency obviously, and can effectively converge to the global optimal solution, consequently providing a new solution to the optimization problem of composite structures.