针对传统多目标优化算法的不足,提出了基于拥挤距离的多目标粒子群优化算法(MOPSO-CD),该算法用拥挤距离来维持精英策略和选取全局极值,同时引入动态非均匀变异算子,用以维持粒子多样性、减缓算法收敛速度、避免早熟收敛。以漳河水库为例,建立了以灌溉缺水量最小和发电量最大为目标函数的两目标优化调度模型,将MOPSO-CD应用于模型的求解中,得到了足够多且较均匀的非劣(Pareto)解前端。 To overcome the shortcomings of traditional multi-objective optimization algorithms, a multi-objective particle swarm optimization algorithm based on congestion distance (MOPSO-CD) is proposed. This algorithm uses crowding distance to maintain the elitist strategy and select the global extremum. At the same time, the dynamic inhomogeneous mutation operator , To maintain particle diversity, slow down the convergence rate of the algorithm and avoid premature convergence. Taking Zhanghe Reservoir as an example, a two-objective optimization scheduling model is established, which takes the minimum irrigation water deficit and the maximum generation as the objective function. MOPSO-CD is applied to solving the model, and sufficient and uniform non-inferior (Pareto) solution front.