Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization

Shixin Huang, Kedao Zhang, Hongmei Li, Xiangjian Chen and Diego Oliva

Mathematical Problems in Engineering, 2023, vol. 2023, 1-10

Abstract: The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing production costs, and improving the quality of parts. Therefore, the research on cutting parameter optimization has important theoretical value and application value. In this paper, in order to find the optimal cutting parameters, the cutting model is solved by the improved monarch butterfly optimization (IMBO) algorithm, and the optimized cutting parameters are obtained. By establishing the mathematical model of cutting, the constraint conditions of actual machining are introduced into the model. In order to solve the model, some ideas of particle swarm optimization (PSO) and differential evolution (DE) are added to the traditional monarch butterfly optimization (MBO) algorithm. The MBO algorithm is improved to deal with multiobjective optimization problems. The IMBO algorithm is used to optimize the cutting model. The experiment shows that the optimized cutting parameters can significantly reduce production cost and maintain high production efficiency. Compared with NSGA-II algorithm and other swarm intelligence optimization algorithms, it shows that the IMBO algorithm has certain advantages in multiobjective optimization.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1348624

DOI: 10.1155/2023/1348624

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