In this paper, a hybrid algorithm called Group Search Particle Swarm Optimization (GSPSO) based on the Particle Swarm Optimization (PSO) and the Group Search Optimization (GSO) is proposed, in which the PSO model and the GSO model are used in turn. The GSPSO combines the advantages of the two algorithms, one is the high computing speed of the PSO and the other is the good performance of the GSO for high-dimension problems. In the GSPSO, the PSO model is used to find a good local search space, which the global optimization point is contained with high probability, and the GSO model is used to search in the local search space, and the rangers are used to revise the local search space at the same time. A mutual rescue method is also proposed for switching the two models. Moreover, a mechanism of weeding out the weak members is also established to increase the diversity of particles. Four benchmark functions are used to evaluate the performance of the novel algorithm. The results show that the GSPSO has better convergence accuracy for high-dimension problems and most low-dimension problems compared to other four algorithms.
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