Multi-objective differential evolution algorithm with fuzzy inference-based adaptive mutation factor for Pareto optimum design of suspension system

摘要

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.