Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimization

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evolutionary process. By testing on 4 benchmark functions, the numerical results demonstrate that, the optimization performance of the proposed GSPSO is much better than PSO and SFLA. The GSPSO can both avoid the PSO’s shortage that easy to get rid of the local optimal solution and has faster convergence speed and higher convergence precision than the PSO and SFLA.