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An enhanced simulation-based design method coupled with meta-heuristic search algorithm for accurate reliability-based design optimization

机译：一种改进的基于仿真的设计方法，结合元启发式搜索算法，可实现基于可靠性的精确设计优化

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摘要

The enhanced weighted simulation-based design method in conjunction with particle swarm optimization (PSO) is developed as a pseudo double-loop algorithm for accurate reliability-based design optimization (RBDO). According to this hybrid method, generated samples of weighed simulation method (WSM) are considered as initial population of the PSO. The proposed population is then employed to evaluate the safety level of each PSO swarm (design candidates) during movement. Using this strategy, there is no required to conduct new sampling for reliability assessment of design candidates (PSO swarms). Employing PSO as the search engine of RBDO and WSM as the reliability analyzer provide more accurate results with few samples and also increase the application range of traditional WSM. Besides, a shift strategy is also introduced to increase the capability of the WSM to investigate general RBDO problems including both deterministic and random design variables. Several examples are investigated to demonstrate the accuracy and robustness of the method. Results demonstrate the computational efficiency and superiority of the proposed method for practical engineering problems with highly nonlinear and implicit probabilistic constrains.
机译：结合粒子群优化（PSO）的增强型基于加权仿真的设计方法被开发为用于基于可靠性的精确设计优化（RBDO）的伪双环算法。根据此混合方法，将加权模拟方法（WSM）的生成样本视为PSO的初始种群。然后，使用提议的种群来评估运动过程中每个PSO群（设计候选者）的安全级别。使用此策略，不需要进行新的采样来评估设计候选（PSO群）的可靠性。使用PSO作为RBDO的搜索引擎，而使用WSM作为可靠性分析器，可以用较少的样本获得更准确的结果，并扩大了传统WSM的应用范围。此外，还引入了转移策略以提高WSM调查包括确定性和随机设计变量在内的一般RBDO问题的能力。研究了几个例子，以证明该方法的准确性和鲁棒性。结果证明了该方法在具有高度非线性和隐含概率约束的实际工程问题中的计算效率和优越性。

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