The adjustment of parameters in particle swarm optimization (PSO) has attracted the attention of many researchers.In the paper

an alternative technology

a non-parameter PSO algorithm with fast convergence speed is proposed.A multi-crossover operation and an exemplar-based learning strategy are combined with the proposed algorithm.According to the first-and second-order stability analyses conducted for the present study

the particle positions are expected to converge at a fixed point in the search space

and the variance of the particle positions converge at zero.In our experiments

we compared the proposed algorithm with 7 other advanced PSO algorithms using 24 widely used benchmark functions.The experimental results indicate that the proposed algorithm yields better solution accuracy than the other PSO algorithms.In particular

the proposed algorithm outperforms the other PSO approaches significantly in terms of the convergence speed.