复合材料层压板热屈曲优化设计的自适应免疫遗传算法

Adaptive immunity genetic algorithm for thermal buckling optimization of composite laminates

复合材料层压壁板的热屈曲优化问题是高速飞行器结构设计的重点考虑内容。通过对免疫遗传算法引入自适应交叉和变异,构造了一种自适应免疫遗传算法(AIGA),并将该算法应用于考虑强度约束的层压板热屈曲铺层顺序优化设计。并将算法的优化结果与简单遗传算法(SGA)、免疫遗传算法(IGA)的优化结果进行了比较,结果表明该算法收敛速度快,优化解的质量最好,并有效的克服了SGA易于早熟收敛,IGA收敛缓慢的缺点。同时研究了抗体调节系数对AIGA算法性能的影响。

Thermal buckling optimization of composite laminates subject to temperature rise is critical consideration during structural design of hypersonic vehicles. An adaptive immunity genetic algorithm （AIGA） was constructed by introducing adaptive crossover and mutation into an Immunity GA （IGA）. The algorithm was applied to solve the stacking sequence optimization of laminates with strength failure constraints. Optimization results were compared to the results found by simple GA （SGA） and IGA. It shows that the proposed algorithm has the most rapidly convergence and the best quality among the three ,and makes much improvement on overcoming the problem of prematurely convergence in SGA and slack convergence in IGA. Moreover the primary controls parameter of ALGA, antibody adjusting factor, was analyzed, in order to find its relationship with the algorithm performance.