基于自适应蚁群算法的水电站水库优化调度

应用自适应蚁群算法来求解水电站优化调度问题,该算法把问题解抽象为蚂蚁路径,利用状态转移,信息素更新和邻域搜索以获取最短路径。实例计算表明,该算法计算精度高,收敛速度快,克服了传统蚁群算法计算时间长,易于陷入局部最优的缺点,能较好地避免动态规划的维数灾问题,可求解具有复杂约束条件的非线性规划问题,为解决水电站优化调度问题提供了一种有效的途径。

Optimal scheduling of hydroelectric power plant based on self-adaptive ant colony algorithm

A novel self-adaptive ant colony algorithm (SA-ACA) was presented to solve optimal scheduling problem for hydroelectric power plant. The SA-ACA is abstracted as the ant path from the solution of problem. The state transition rule, global updating information and the neighbor search are also employed to get the optimal scheduling. The case computational results show that SA-ACA has the advantages of the higher calculating accuracy and the faster convergence speed, and overcome the shortcomings of the longer computation time, easily dropping into partial optimization and dimension number calamity. SA-ACA is an effective method for solving nonlinear optimal problem with complicated strict terms, and provides an applied approach to solve the optimal scheduling problem with hydroelectric power plant.