Multiple hydropower reservoirs operation is an effective measure to rationally allocate the limited water resources under uncertainty. With the rapid expansion of water resources system, it becomes much more difficult for traditional methods to quickly yield the reasonable operational policy. Grey wolf optimizer, inspired by the wolves’ hunting behaviors, is a famous metaheuristic method to resolve engineering optimization problems, but still suffers from the local convergence and search stagnation defects. To alleviate this problem, this study proposes a hybrid grey wolf optimizer (HGWO) where the hyperbolic accelerating strategy is introduced to improve the local search ability; the adaptive mutation strategy is used to diversify the swarm; the elitism selection strategy is used to enhance the convergence speed. The experimental results show that the HGWO method can produce better solutions than its original version in several test functions. Then, the HGWO method is applied to resolve the optimal operation of a real-world hydropower system with the goal of maximizing the total generation benefit. The simulations indicate that the HGWO method produces satisfying scheduling schemes than several control methods in terms of all the statistical indicators. Hence, with the merits of superior search ability, rapid convergence rate and gradient information avoidance, HGWO proves to be a promising alternative optimization tool for the complex multireservoir system operation problem.

多水库运行是在不确定性条件下合理分配有限水资源的有效措施。随着水资源系统的迅速扩展，传统方法难以迅速产生合理的运营政策变得更加困难。受狼的狩猎行为启发的灰太狼优化器是一种解决工程优化问题的著名的元启发式方法，但仍然遭受局部收敛和搜索停滞缺陷的困扰。为了缓解这个问题，本研究提出了一种混合灰狼优化器（HGWO），其中引入了双曲线加速策略以提高局部搜索能力。自适应变异策略用于使种群多样化；精英选择策略用于提高收敛速度。实验结果表明，在几个测试函数中，HGWO方法可以提供比原始方法更好的解决方案。然后，以最大化总发电收益为目标，应用HGWO方法解决现实世界水电系统的最优运行。仿真表明，就所有统计指标而言，与几种控制方法相比，HGWO方法可产生令人满意的调度方案。因此，HGWO具有搜索能力强，收敛速度快和避免梯度信息的优点，被证明是解决复杂的多水库系统运行问题的有希望的替代优化工具。HGWO方法用于解决实际水电系统的最佳运行问题，目的是最大化总发电收益。仿真表明，就所有统计指标而言，与几种控制方法相比，HGWO方法可产生令人满意的调度方案。因此，HGWO具有搜索能力强，收敛速度快和避免梯度信息的优点，被证明是解决复杂的多水库系统运行问题的有希望的替代优化工具。HGWO方法用于解决实际水电系统的最佳运行问题，目的是最大化总发电收益。仿真表明，就所有统计指标而言，与几种控制方法相比，HGWO方法可产生令人满意的调度方案。因此，HGWO具有搜索能力强，收敛速度快和避免梯度信息的优点，被证明是解决复杂的多水库系统运行问题的有希望的替代优化工具。