Abstract: This paper proposes Modified Adaptive Particle Swarm Optimization(MAPSO). It introduces population entropy to judge whether the algorithm falls in local peak or not and dynamically changes the inertia weight. It is applied in solving the problem of single reservoir operation optimization. According to the mathematic model, the detailed steps based on MAPSO are put forward. By calculations of the example and comparison with PSO and APSO, it proves the algorithm has higher convergence speed, and its convergence performance is improved by 1.13 percent compared with GA. It certifies that the method is feasible and valid.

Key words: operation optimization, Particle Swarm Optimization(PSO), reservoir, entropy

摘要： 提出改进的自适应粒子群优化算法(MAPSO)，引入种群熵判断粒子群优化算法(PSO)是否陷入局部最优，动态改变算法惯性权重，并将该算法用于单个水库的优化调度。建立水库优化调度的数学模型，给出基于MAPSO算法的水库优化调度的实现步骤。仿真实验证明，讲该算法用于水库的优化调度是可行、有效的，与PSO、APSO相比，收敛速度更快，与遗传算法相比，性能提高了1.13%。

关键词: 优化调度, 粒子群优化算法, 水库, 熵

CLC Number: 

• TP301.6