Diversity based hybrid particle swarm algorithm

As an important branch of swarm intelligence (SI), particle swarm optimization (PSO) has been successfully applied to a wide range of applications. Aiming at solving the premature convergence problem, a hybrid PSO variant is proposed in the paper. Not to improve population diversity merely, the proposal adaptively select different evolutionary scheme according to the value of population diversity. Therefore, the proposed method can balance between the global and local exploration capabilities. The experimental results indicate that the proposed algorithm yields a better solution accuracy and convergence speed than the other PSO techniques. In terms of the convergence speed, in particular, the proposed algorithm outperforms most other PSO approaches significantly.