The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentumPSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO (LDWPSO) equipped with a boundary checking strategy is also discussed, which is denoted as LDWBCPSO. LDWPSO, LDWBCPSO and momentumPSO are compared in optimization on five test functions. The experimental results show that in some special cases LDWPSO finds invalid solutions and LDWBCPSO has poor performance, while momentumPSO not only exhibits good performance but also reduces computational cost for updating velocities. The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A, in order to invalid solutions in some special cases. a new strategy about updating velocities is given. The resulting approach is mromentumPSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO (LDWPSO) equipped with a boundary checking strategy is also discussed , which is denoted as LDWBCPSO. LDWPSO, LDWBCPSO and momentumPSO are compared in optimization on five test functions. The experimental results show that in some special cases LDWPSO found invalid solutions and LDWBCPSO has poor performance, while momentumPSO not only exhibits good performance but also reduces computational cost for updating velocities.