In order to address the optimization of parameters in hot rolling production processes,a dif-ferential evolution algorithm is proposed.Firstly,a random forest regression model is constructed to predict quality characteristic values.Then,based on the feature importance ranking results of the ran-dom forest regression model,the process parameters to be optimized are selected.Finally,the differen-tial evolution algorithm is used to optimize the parameters and the optimal results are used to replace the process parameters to be optimized.The average reduction value and average reduction rate of de-fect occurrence rate are proposed as evaluation indicators.Comparative studies of the results before and after optimization show that compared to other optimization algorithms,the differential evolution algo-rithm achieves optimal performance.Application analysis demonstrates that after optimizing process pa-rameters with the proposed algorithm,the number of defects in the unit slab is less than 25,and the average execution time of the algorithm is 8.57 s,indicating its good operability and practical value.
hot rollingprocess parameter optimizationdifferential evolutionrandom forest
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