A hierarchical bare bones particle swarm optimization algorithm

Abstract

The bare bones particle swarm optimization (BBPSO) is a population-based algorithm. The BBPSO is famous for easy coding and fast applying. A Gaussian distribution is used to control the behavior of the particles. However, every particle learning from a same particle may cause the premature convergence. To solve this problem, a new hierarchical bare bones particle swarm optimization algorithm is proposed in this work. Three random particles are placed in one group and exchanging information during the iteration process. And a hierarchical method is used in every group. Therefore, the swarm gains an increasing of diversity and more chances to escape from the local optimum. Moreover, a mutated structure for the local group is presented in this paper. To verify the ability of the proposed algorithms, a set of well-known benchmark functions are used in the experiment. Also, to make the experiment more persuasive, several evolutionary computation algorithms are applied to the same functions as the control group. The experimental results show that the proposed algorithms perform well in the test functions.