In this research, novel genetic algorithm (nGA) is proposed for Bouc-Wen modle parameters esstimation for magnetorheological (MR) fluid dampers. The optimization efficiency is improved by modifying the crossover and mutation steps of a GA. In the crossover stage, the probability of reproducing offspring from the same parent (same mother and father chromosome) is done to be zero, which may happen in the standard GA, and the probability of a chromosome to be selected for mating is based on error rank weighting of the chromosomes. Additional fitness evaluation of chromosomes will take place in between the crossover and mutation steps to save the best chromosome found so far, which is not implemented in the standard genetic algorithm (GA). The model is validated by comparing its simulation output force (F_sim) with experimentally generated MR damper force (F_exp). The mean absolute error, standard deviation and number of generations for convergence are taken as a criterias for performance evaluation. With these ctriterias, the proposed novel GA outperform better than the other researches. The accuracy is improved by 46.67% compared to standard GA. The proposed novel GA for Bouc-Wen model parameter identification can be used for any MR damper control system with better accuracy.

在这项研究中，提出了一种新的遗传算法（nGA）用于磁流变（MR）流体阻尼器的Bouc-Wen模型参数估计。通过修改GA的交叉和变异步骤，可以提高优化效率。在交叉阶段，从同一父代（相同的父与父染色体）繁殖后代的概率被确定为零，这在标准GA中可能会发生，并且选择染色体进行交配的概率是基于误差的染色体的等级加权。染色体的适应性评估将在交叉和突变步骤之间进行，以保存迄今为止发现的最佳染色体，这在标准遗传算法（GA）中未实现。通过比较模型的模拟输出力（F _sim），并通过实验产生的MR阻尼力（F _exp）。平均绝对误差，标准偏差和收敛的代数被视为性能评估的标准。利用这些标准，提出的新型遗传算法优于其他研究。与标准GA相比，准确性提高了46.67％。提出的用于Bouc-Wen模型参数识别的新颖GA可用于任何具有更好精度的MR阻尼器控制系统。