In this paper, a multi-objective differential evolution with fuzzy inference-based dynamic adaptive mutation factor (MODE-FM) is proposed for Pareto optimization of problems using a combination of non-dominated sorting and crowding distance. In the proposed algorithm, fuzzy inference is employed to dynamically tune the mutation factor for a better exploration and exploitation ability. In the proposed work, to adapt the mutation factor, the generation count and population diversity in each generation are provided as inputs to fuzzy inference system and the mutation factor is obtained as an output. Performance of the suggested approach is first tested on popular benchmark functions adopted from IEEE CEC 2009. Secondly, vehicle vibration model with five degrees of freedom is selected to be optimally designed by the aforesaid proposed approach. Comparison of the obtained results of this work with those in the literature has confirmed the superiority of the proposed method.

本文提出了一种基于模糊推理的动态自适应变异因子（MODE-FM）的多目标差分进化算法，该算法结合了非支配排序和拥挤距离，可以对问题进行帕累托优化。该算法采用模糊推理对突变因子进行动态调整，具有更好的勘探开发能力。在拟议的工作中，为了适应突变因子，将世代计数和每一代中的种群多样性作为模糊推理系统的输入，并获得突变因子作为输出。首先在IEEE CEC 2009所采用的流行基准功能上测试了所建议方法的性能。其次，选择了具有五个自由度的车辆振动模型以通过上述建议方法进行最佳设计。