Hybrid approach for genetic algorithm and Taguchi's method based design optimization in the automotive industry

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Abstract
Although genetic algorithm and multi-objective optimization techniques are widely used to solve problems in the design and manufacturing area, further improvements are required to develop more efficient techniques regarding multi-objective optimization problems. The main goal of the present research is to further develop and strengthen the genetic algorithm based multi-objective optimization approach to generate real-world design solutions in the automotive industry. In this research, a new hybrid approach based on Taguchi's method and a genetic algorithm is presented to achieve better Pareto-optimal set solutions for multi-objective design optimization problems. In addition, fatigue damage and life are also considered to evaluate the results of the design optimization process. The validity and efficiency of the proposed approach are evaluated and illustrated with test problems taken from the literature. It is then applied to a vehicle component taken from the automotive industry.

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