Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran; Department of Biological and Agricultural Engineering, University of California, Davis, UC Davis, CA, USA.
School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
J Environ Manage. 2021 May 15;286:112250. doi: 10.1016/j.jenvman.2021.112250. Epub 2021 Mar 19.
The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models' performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.
对水的持续增长的需求、长时间的干旱以及主要归因于气候变化的气候不确定性意味着地表水水库比以往任何时候都更需要有效地管理。已经开发了几种优化算法来优化多水库系统的运行，主要是在严重的干旱/湿润季节，以减轻极端事件的后果。然而，在寻找全局最优解时，收敛速度、存在局部最优解和计算成本效率是具有挑战性的。在本文中，讨论了在多水库系统中寻找有效最优运行策略的问题。长期运行规则的复杂性以及递归约束中水库上下游联合需求使得这个问题非常棘手。原始的珊瑚礁优化（CRO）算法是一种启发式进化算法，以及两个修改版本已被用于解决这个问题。提出的修改通过缩小搜索空间（称为约束 CRO）并使用强化学习方法（即 Q-学习方法）调整繁殖算子来降低计算成本，即 CCRO-QL 算法。修改后的版本在可行区域而不是整个问题域中搜索最优解。通过解决五个数学基准问题和一个著名的连续四水库系统（CFr）问题来评估模型的性能。将获得的结果与文献中的结果以及线性规划（LP）实现的全局最优值进行了比较。CCRO-QL 在定位全局最优或近优解以及在收敛性、准确性和鲁棒性方面都非常高效且具有成本效益。
