Multi‐Strategy Fusion Binary Zebra Optimization Algorithm for Solving Complex Industrial Process Data Feature Selection and Prediction Models

ABSTRACT In the process of modern industrial production, soft sensing technology is often used to predict the target variables that are difficult to be directly measured by hard instruments. However, the input variables used for prediction are not all closely related to the output. Feature selection (FS) aims to select the features highly related to the target variables and discard the redundant features. How to select the optimal feature subset and reduce the operation cost while ensuring the prediction accuracy becomes a key problem. A multi‐strategy fusion binary Zebra optimization algorithm (MFBZOA) was proposed to select the optimal feature subset in a prediction model. Firstly, the Cauchy inverse cumulative distribution function is used to mutate individual positions in the defense stage of the zebra optimization algorithm (ZOA), and then reproductive behavior is introduced into the algorithm to increase the diversity of the solution set and improve the overall quality of the solution. Finally, the Cauchy mutation strategy is introduced to disturb the worst individuals in the zebra population and increase the probability of jumping out of the local optimum. Firstly, the proposed improved ZOA is combined with the Zebra optimization algorithm, Golden sine algorithm, Whale optimization algorithm, Frilled lizard optimization, Human evolution optimization algorithm, Coatis optimization algorithm, and Goose optimization algorithm to perform CEC2022 function optimization simulation experiments to verify its effectiveness. Then, MFBZOA and the above comparison algorithms are used as search strategies respectively, combined with the wrapper FS method driven by a multi‐layer perceptron to solve the FS problem of four industrial process data and build the corresponding prediction model. Then, the optimal feature subset selected by each algorithm is used in the prediction experiment. The simulation results show that MFBZOA can effectively select the optimal feature subset, improve the global search ability and local search ability, and maintain good prediction accuracy and generalization performance.