动态蚁群算法在梯级水库优化调度中的应用

针对蚁群算法缺乏全局搜索与局部寻优之间的动态调整，蚁群难以保持较好的多样性，算法极易陷入局部最优的问题，以余弦递减的策略动态调整启发式因子的变化，从而平衡算法的全局搜索和局部寻优。同时，利用混沌搜索的随机性和遍历性，对蚁群每次迭代找到的路径进行混沌扰动，从而提高算法跳出局部最优的能力，避免算法早熟收敛。将改进的蚁群算法应用于梯级水库的联合优化调度，模拟计算结果表明了算法的有效性。将其结果与逐步优化法和标准蚁群算法的计算结果进行对比，体现了算法在求解速度和求解精度上的优势。

Application of dynamic ant colony algorithm on the optimal operation of cascade reservoirs

Abstract：Since the lack of dynamic adjustment between global search and local optimization in traditional ant colony algorithm, it is difficult to keep the diversity of ant colony and easy to fall into local minima. In order to avoid premature convergence, the heuristic factor is adjusted according to cosine decline and the search route in each iteration is disturbed by chaotic search for its randomicity and ergodicity in this study. Then the modified algorithm is applied to the optimal operation of cascade reservoirs. Results show the validity of the algorithm and the comparison with progressive optimization algorithm and traditional ant colony algorithm indicate that the modified algorithm has the advantage of speed and accuracy in solving problem.