A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization (IMOPSO) is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions (A, B, C) of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-II (NSGA-II), IMOPSO has close global op- timization capability and is suitable for multi-objective optimization problems. A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization (IMOPSO) is proposed. The IMOPSO is employed to solve the optimal model and obtains the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions (A, B, C) of the Pareto-optimal front correspond to the optimized schemes in terms of the Objective of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydro Compared with the non-dominated sorting genetic algorithm-II (NSGA-II), IMOPSO has close global opti- mization capability and is suitable for multi-objective optimization problems.