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针对梯级水库群优化调度问题的特点,建立蚁群算法求解多阶段最优化问题数学模型.把水库的运行策略转换为水库水位变化序列,通过一定的编码形式分别将其表示人工蚂蚁的路径.人工蚂蚁在满足一定的约束条件下,按预定的目标函数评价其优劣.针对蚁群算法在优化过程中出现搜索时间较长和早熟停滞现象,提出了具有变异特征混合局部优化算法的蚁群系统(MSA-ACS).然后将MSA-ACS和蚁群系统(ACS)分别用于求解雅砻江梯级优化调度问题,通过对优化结果和计算时间的对比分析,验证了改进方法的有效性.该改进方法获得了比较满意的解,不仅能提高蚁群算法的收敛性能,还能增强解的稳定性.
In view of the characteristics of the optimal scheduling problem of cascade reservoirs, an ant colony algorithm is proposed to solve the mathematic model of multi-stage optimization problem, and the operational strategies of reservoirs are transformed into the sequence of reservoir water level changes. Under certain constraints, ants evaluate its advantages and disadvantages according to a predetermined objective function.According to the fact that Ant Colony Algorithm (ACO) has a long search time and premature stagnation phenomenon in the optimization process, an ant colony system with mutation characteristic hybrid local optimization algorithm (MSA-ACS) .At the same time, MSA-ACS and ant colony system (ACS) are respectively used to solve the problem of the Yalong River cascade optimization scheduling problem. The comparison between the optimization results and calculation time shows that the proposed method is effective. The improved method obtains a satisfactory solution, which can not only improve the convergence performance of ant colony algorithm, but also enhance the stability of the solution.