Aiming at the problem that the basic Multi-Objective Particle Swarm Optimization(MOPSO) algorithm may lead to local optimum and the inability to output representative non-dominated solutions, this paper uses latin hypercube sampling and clustering analysis to improve the algorithm based on MOPSO algorithm. Firstly, the latin hypercube sampling is used to guide the MOPSO algorithm population initialization, so that the initial population can be evenly distributed throughout the space and avoids the local optimization problems that the basic MOPSO algorithm may cause. Secondly, the output solution sets are clustered to select representative non-dominant solutions,which purpose is to enhance the distribution and diversity of non-dominant solution sets. Meanwhile, this method can automatically select the representative non-dominant solutions among many non-dominant solutions . Compared with the basic MOPSO algorithm, the Pareto solution set for improved MOPSO algorithm has certain advantages in the effect of optimization and the selection of representative solutions.