Banyan tree growth optimization and application

Xian Wu, Wenju Zhou, Minrui Fei, Yonglu Du, Huiyu Zhou

Abstract: In the era of big data, the number of science and technology service resources has proliferated, and the integration and clustering of science and technology documents become a challenging issue. This paper proposes a novel meta-heuristic algorithm, banyan tree growth optimization (BTGO), for resource clustering of science and technology services. The proposed algorithm is inspired by the growth process of banyan tree, which periodically uses three operators including rooting, multi-trunk, and adjustment to search the solution space globally according to the growth conditions of different stages. To evaluate the performance of BTGO, 29 CEC17 benchmark functions were first utilized to examine its effectiveness. Moreover, a clustering study on UCI datasets is then presented, which compares the suggested algorithm with seven advanced metaheuristic optimization algorithms. The results of numerical experiments and standard datasets demonstrate the effectiveness and efficiency of BTGO. In clustering optimization problems, BTGO can not only finding the optimal solution efficiently, but also improving the clustering accuracy and NMI significantly. Our method was successfully applied to solve the science and technology text clustering problem and validated on the Hainan Science and Technology Service Experimental Platform.