Abstract: An iterative particle swarm algorithm is proposed for the robust optimization problem of batch process-es without state independent and end-point constraints,which combines the iteration method and the particle swarm optimization algorithm together.For the algorithm,the control variables are discretized firstly and the standard particle swarm optimization algorithm is used to search for the best solution of the discretized control variables.Second,the benchmark is moved to the acquired optimal values in the subsequent iterations and the searching space gets contracted at the same time;hence the optimization performance index and control profile can achieve the best value gradually through iterations.The algorithm is simple,feasible and efficient,and avoids the problem of solving large-scale differential equation group.The simulation results of a batch process shows that the iterative particle swarm algorithm can solve the robust optimization problems of batch processes effectively if there is no state independent and end-point constraints.