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New simulation-based frameworks for multi-objective reliability-based design optimization of structures

机译：新的基于仿真的框架，用于基于多目标可靠性的结构设计优化

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In the present study, two new simulation-based frameworks are proposed for multi-objective reliability-based design optimization (MORBDO). The first is based on hybrid non-dominated sorting weighted simulation method (NSWSM) in conjunction with iterative local searches that is efficient for continuous MORBDO problems. According to NSWSM, uniform samples are generated within the design space and, then, the set of feasible samples are separated. Thereafter, the non-dominated sorting operator is employed to extract the approximated Pareto front. The iterative local sample generation is then performed in order to enhance the accuracy, diversity, and increase the extent of non-dominated solutions. In the second framework, a pseudo-double loop algorithm is presented based on hybrid weighted simulation method (WSM) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that is efficient for problems including both discrete and continuous variables. According to hybrid WSM-NSGA-II, proper non-dominated solutions are produced in each generation of NSGA-II and, subsequently, WSM evaluates the reliability level of each candidate solution until the algorithm converges to the true Pareto solutions. The valuable characteristic of presented approaches is that only one simulation run is required for WSM during entire optimization process, even if solutions for different levels of reliability be desired. Illustrative examples indicate that NSWSM with the proposed local search strategy is more efficient for small dimension continuous problems. However, WSM-NSGA-II outperforms NSWSM in terms of solutions quality and computational efficiency, specifically for discrete MORBDOs. Employing global optimizer in WSM-NSGA-II provided more accurate results with lower samples than NSWSM.
机译：在本研究中，针对基于多目标可靠性的设计优化（MORBDO），提出了两个基于仿真的新框架。第一种是基于混合非主导排序加权模拟方法（NSWSM）以及对连续MORBDO问题有效的迭代局部搜索。根据NSWSM，在设计空间内生成均匀样本，然后分离出可行样本集。此后，使用非支配的分类算子提取近似的Pareto前沿。然后执行迭代局部样本生成，以提高准确性，多样性并增加非支配解的范围。在第二个框架中，提出了一种基于混合加权仿真方法（WSM）和非支配排序遗传算法II（NSGA-II）的伪双循环算法，该算法可有效解决离散变量和连续变量两个问题。根据混合WSM-NSGA-II，在每一代NSGA-II中都会生成正确的非支配解，然后WSM评估每个候选解的可靠性级别，直到算法收敛到真正的Pareto解。提出的方法的宝贵特征是，即使需要针对不同级别可靠性的解决方案，WSM在整个优化过程中也只需要进行一次仿真运行。说明性示例表明，具有建议的局部搜索策略的NSWSM对于小尺寸连续问题更有效。但是，WSM-NSGA-II在解决方案质量和计算效率方面优于NSWSM，特别是对于离散MORBDO。与NSWSM相比，在WSM-NSGA-II中使用全局优化器可提供更准确的结果，且样本数量更少。

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