STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems

Abstract

Tree-Seed Algorithm (TSA) has good performance in solving various optimization problems. However, it is inevitable to suffer from slow exploitation when solving complex problems. This paper makes an intensive analysis of TSA. In order to keep the balance between exploration and exploitation, we propose an adaptive automatic adjustment mechanism. The number of seeds can be defined in the initialization process of the optimization algorithm. In order to further improve the convergence rate of TSA, we also modify the change model of seed numbers in the initialization process with randomly changing from more to less. With the improvement of two mechanisms, the main weakness of TSA has been overcome effectively. Based on the above two improvements, we propose a new algorithm-Sine Tree-Seed Algorithm (STSA). STSA achieves good results in solving high-dimensional complex optimization problems. The results obtained from 24 benchmark functions confirm the excellent performance of the proposed method.