Finding the optimal codebook is one of the key problems for interference alignment with limited feedback, it is equivalent to line packing issue in the Grassmannian manifold. Because analytical construction of the optimal codebook is possible only in very special cases, numerical search algorithms or generalized vector quantization algorithms for source coding are often sought to obtain near-optimal codebooks, but these algorithms characterize with poor performance and high complexity. In order to reduce the complexity of codebook construction, a new accelerative Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm is proposed. The convergence rate during the early period of the algorithm is speeded by studying of the best particle, the convergence rate during the later period is speeded and the performance of the algorithm is improved through reduction the maximum velocity of particles based on the CLPSO algorithms advantage of easy implementation, performing well on searching the optimal solution within defined space for non-linear problems, especial for complex multimodal problems. The simulation results show that the new algorithm achieves better performance than Particle Swarm Optimization (PSO), CLPSO and Generalized Lloyd Algorithm (GLA) with low computational?complexity.