基于非支配排序遗传算法的多学科鲁棒协同优化方法

Multidisciplinary robust collaborative optimization based on non-dominated sorting genetic algorithm

中文关键词  鲁棒协同优化  NSGA--II算法  多目标  学科间一致性

英文关键词  robust collaborative optimization  NSGA--II algorithm  multiobjective  interdisciplinary consistency

中文摘要

      针对鲁棒协同优化(robust collaborative optimization, RCO)具有两级优化结构和多目标形式的特点, 提出基于非支配排序遗传算法(non-dominated sorting genetic algorithm, NSGA--II)的RCO求解方法. 在NSGA--II非支配排序中, 根据个体的不可行度和不可行度阈值来决定其可行性, 并给出随进化过程逐渐减小的不可行度阈值. 在该阈值的作用下, 在进化初期, 保留较多的目标函数和标准差较小的个体, 以便优化向全局极值点附近靠近; 在进化后期, 保留较多的学科间一致性好的个体, 以便增强学科间的一致性. 该方法在保证各子学科间一致性的前提下, 可有效避免RCO优化结果易收敛到局部极值点的问题. 利用典型算例对该方法进行了验证, 结果表明该方法的优化性能良好.

英文摘要

      To the robust collaborative optimization(RCO) scheme with two-level multiobjective optimization structure, a solution strategy employing the non-dominated sorting genetic algorithm(NSGA--II) is proposed. In the process of non-dominated sorting, the feasibility of an individual is determined by its infeasibility degree and the threshold of infeasibility degree. The threshold of infeasibility degree is reduced gradually in the process of evolution. At the initial stage of genetic evolution, the individuals with smaller values of objective function and standard deviation are more likely to be preserved to ensure the optimization process for reaching the neighborhood of the global extremum. In the following stages of genetic evolution，the individuals with smaller value of infeasibility degree are more likely to be preserved to enhance the interdisciplinary compatibility. The convergence of the results of RCO to the local extremum is usually avoided while keeping the desired interdisciplinary consistency. The results of validation by using typical examples show that the proposed approach is efficient.