A Dynamic Reconstruction Bare Bones Particle Swarm Optimization Algorithm

Abstract

The bare bones particle swarm optimization algorithm is a useful method for the optimization problems. Each individual particle has been given memories to recorded its personal best position. The best of all personal best positions is recorded as the global best position by the particle swarm. A Gaussian distribution is used to control the behavior of the particles according to the personal and the global best position. However, this iterative pattern weak at multimodal problems. Particles are easy to be trapped in the local minimums. To cross this shortcoming, the dynamic reconstruction bare bones particle swarm optimization algorithm (DRBBPSO) is proposed in this work. The dynamic reconstruction strategy is used to enhance the global search ability of the particle swarm. Numbers of elite particles are selected to reconstruct the particle swarm. To verify the performance of the proposed algorithm, a set of comprehensive benchmark functions are used in the experiments. Also, several swarm-based algorithms including the standard bare bone particle swarm optimization algorithm are used in the control group. The experimental results confirmed the searching ability of the DRBBPSO.