To enhance the optimization ability of the particle swarm optimization (PSO), an improved PSO algorithm was proposed in this paper. In the proposed approach, the Beta distribution function is used to initialize population, and the inverse incomplete gamma function is used to update the inertia weight. For adjustment of velocity, a new operator based on differential evolution is introduced. For cross-border processing of particles, a new method based on boundary symmetry mapping is designed. With taking 50 different types of benchmark functions as optimization examples, the experimental results based on the Wilcoxon-Signed rank test show that the proposed algorithm is obviously superior to the common PSO, differential evolution, attificial bee colony algorithm and quantum-behaved particle swarm optimization algorithm.