Optimization Strategy for Industrial Machinery Product Selection Scheme Based on DMOEA

Abstract

With the continuous innovation and replacement of industrial machinery products, the traditional optional configuration plans are no longer able to complete product selection work with high quality. To further optimize the product selection process and solve the multi-objective selection problem of industrial machinery products, a multi-objective problem model for product selection is normalized and constructed based on the existing difficulties in industrial machinery product selection. A new product selection model is proposed by introducing a multi-objective evolutionary algorithm based on density calculation for model solving. The experimental results showed that the new model had the highest selection success rate of 97% and selection accuracy close to 95% when the iterations were 250. In addition, the maximum absolute error sum of the selected bearing and bearing seat diameters under this model was 0.002. The maximum relative error was 0.01%. The highest reliability of algorithm fitting was 99.9%. Simulation tests found that the average selection success rate was 93%. The average selection quality loss was 26%. In summary, the new selection model proposed in the study has certain advantages and feasibility. It can provide effective decision-making solutions for the design and selection of industrial machinery products.