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首页> 外文会议>The 7th China-Japan-Korea joint symposium on optimization of structural and mechanical systems >DISCRETE STRUCTURAL DESIGN OPTIMIZATION USING SUBSET SIMULATION ALGORITHM
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DISCRETE STRUCTURAL DESIGN OPTIMIZATION USING SUBSET SIMULATION ALGORITHM

机译：子集仿真算法的离散结构设计优化

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摘要

This article deals with the design optimization of truss structures with discrete design variables, which remains quite a challenging task in structural design.A stochastic searching strategy based on the recently developed subset simulation optimization algorithm and variable transformation method for sampling is presented in details for discrete structural optimization. The discrete design variables are transformed into standard normal variables to implement sampling procedure in subset simulation, while optimization procedure is processed in the discrete design space simultaneously. The performance of the proposed algorithm is illustrated by a benchmark-type optimization problem.Comparisons are made with other well-know stochastic optimization algorithms.It is found that the proposed algorithm is a powerful stochastic optimization for discrete structural optimization, and can produce designs as good as or better than those of other stochastic optimization algorithms.
机译：本文针对具有离散设计变量的桁架结构进行设计优化，这在结构设计中仍然是一项艰巨的任务。详细介绍了一种基于最近开发的子集仿真优化算法和变量采样方法的随机搜索策略。结构优化。离散设计变量被转换为标准正态变量，以在子集仿真中执行采样过程，而优化过程则同时在离散设计空间中进行处理。用基准类型优化问题说明了该算法的性能。与其他众所周知的随机优化算法进行了比较，发现该算法对于离散结构优化是一种强大的随机优化方法，可以产生如下设计结果：优于或优于其他随机优化算法。

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