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Fully Stressed Design Evolution Strategy for Shape and Size Optimization of Truss Structures

机译:用于桁架结构形状和尺寸优化的全应力设计演化策略

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Following the principles of the state-of-the-art Evolution Strategies in continuous optimization, a novel algorithm is introduced which simultaneously optimizes shape and size of truss structures. The algorithm, called Fully Stressed Design Evolution Strategy (FSD-ES), combines advantages of the well-known deterministic approach of Fully Stressed Design and potent global search of Evolutionary Algorithms. Based on available engineering knowledge on truss analysis, a novel adaptive penalty term is also introduced and utilized to reinforce the near-bound search abilities of the algorithm. Following recent advances in empirical evaluation, the performance of FSD-ES is assessed on a reasonable test suite and compared to the best results available in the literature. Performance for the case without grouping members or exploiting symmetry are also reported which significantly increase the problem dimension, leading to more challenging test problems, more discriminating results and more reliable conclusions. Result comparison demonstrates that for more complicated problems FSD-ES reaches the same fitness noticeably faster and finds a relatively lighter structure than those previously reported in the literature.
机译:遵循持续优化中最先进的进化策略的原理,引入了一种新颖的算法,可同时优化桁架结构的形状和尺寸。该算法称为“全应力设计演化策略”(FSD-ES),它结合了众所周知的“全应力设计”确定性方法的优点以及对演化算法的有效全局搜索。基于对桁架分析的可用工程知识,还引入了一种新颖的自适应惩罚项,并将其用于增强算法的近界搜索能力。随着经验评估的最新进展,FSD-ES的性能在合理的测试套件上进行了评估,并与文献中可获得的最佳结果进行了比较。还报告了没有分组成员或没有利用对称性的情况下的性能,这显着增加了问题范围,导致更具挑战性的测试问题,更具区分性的结果和更可靠的结论。结果比较表明,对于更复杂的问题,FSD-ES达到相同适应性的速度明显更快,并且比以前文献中报道的结构更轻。

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