Smart Partitioning for Product DSM Model Based on Improved Genetic Algorithm

Yangjie Zhou, Chao Che, Jianxin Zhang, Qiang Zhang, Xiaopeng Wei

Abstract: Aiming at the coupling analysis of the design structure matrix(DSM), a new partitioning approach(improved genetic algorithm) is proposed. In the method, the fitness function calculates both the elements in the upper triangular matrix and the ones in the lower triangular matrix. Furthermore, the crossover operator is removed and the mutation rate becomes bigger. Minimized the object function including all elements in DSM, the partitioning accuracy is obvious improved. Through the coupling analysis for the DSM between ‘parameter-parameter’ of Axial-Flow Turbocharger, our method outperforms the original genetic algorithm. The result of partitioning validates the efficiency and feasibility of the proposed approach.