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Optimal Placement of MR Dampers For Structural Control Using Identification Crossover Genetic Algorithm

机译:基于识别交叉遗传算法的结构控制MR阻尼器优化布置

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摘要

In order to study the optimal placement of magnetorheological (MR) fluid dampers for structural control, an improved genetic algorithm, i.e. identification crossover genetic algorithm (ICGA), is presented in this paper. The method avoids the constraint violations caused by simple crossover and basic mutation operation. The ICGA produces an identification code. The crossover based on the identification code as well as mutation based on bit-by-bit basis guarantees the fulfillment of constraints. The analytical results by ICGA and general genetic algorithm are compared. It is concluded that the converging speed by ICGA is faster than the converging speed by general genetic algorithm and the control effect by optimal placement is satisfactory.
机译:为了研究用于结构控制的磁流变(MR)流体阻尼器的最佳位置,本文提出了一种改进的遗传算法,即识别交叉遗传算法(ICGA)。该方法避免了由于简单的交叉和基本的变异操作而引起的约束违规。 ICGA产生一个识别码。基于识别码的交叉以及基于逐位的变异保证了约束的实现。比较了ICGA和通用遗传算法的分析结果。结论:ICGA的收敛速度快于通用遗传算法的收敛速度,最优布局的控制效果令人满意。

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