Particle Swarm Optimization (PSO) can easily fall into the local extremum and has slow convergence and low precision in the late evolution. For these reasons, we propose an Improved Particle Swarm Optimization (IPSO) algorithm that integrates multiple strategies. It includes the following four improvements. Firstly, the grouping strategy is adopted. According to the fitness values, the population is divided into an optimal particle group and an inferior particle group, which are subject to crossover and mutation operations, respectively. Secondly, the elite strategy is used to update the population. The first 50% particles are selected from the population after crossover and mutation operations and the initial population according to fitness values and taken as a new population. Thirdly, the particle learning mode is improved to make full use of the population information. The particle best is replaced with the mean of the optimal particle group. Fourthly, probability control is introduced to control the probability of the algorithm’s entering crossover and mutation operations. The simulation results of the test functions show that compared with the standard PSO and its improved variants, the IPSO algorithm can effectively take into account the global exploration and local mining capabilities, and has the advantages of fast convergence, high accuracy, and avoidance from the local optimal solution.