A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

Abstract

AbstractA modified adaptive butterfly optimization algorithm is established with the aim of addressing the “early search blindness” and the relatively poor adaptability of the sensory modality. A normal‐distribution‐based model and a Weibull‐distribution‐based adaptive model of sensory modalities are respectively proposed for the global search process and iteration process. Among them, the Weibull‐distribution‐based adaptive model of sensory modalities is mainly manifested as the c value, that is, the adaptive change trend based on the Weibull model. The performance of the modified butterfly optimization algorithm is validated using a 14‐benchmark test function and compared with performances of some latest algorithms. The experimental results indicate that the modified algorithm performs competitively in terms of accuracy and stability. Following the experiment, the modified algorithm is further tested by running a support‐vector‐machine prediction model based on engineering data of a pipe belt conveyor's flat‐pipe/pipe‐flat transition segment. The results of the modified algorithm are then compared with test‐run outcomes of the back‐propagation prediction model and KCV‐SVM model. The results show that the prediction error is well within 10%, demonstrating the method's competence as a reliable reference for future designs of pipe belt conveyors.