Optimal reservoir operation has become a challenging problem due to streamflow uncertainties. Frameworks for optimal reservoir operation play a vital role in the management of water resources. Effective and judicious utilisation of water from reservoirs helps in the management of periods of water deficit. The main objective in reservoir optimisation is to design operating rules that can be used to derive real-time decisions on reservoir release. In this study, a metaheuristic optimisation algorithm – the parametric elitist cuckoo search algorithm (Pecsa), based on the basic cuckoo search algorithm (CSA) – was applied to develop optimal operation decisions for the RSSR in the MRP, India. To evaluate the performance of Pecsa, the results obtained were compared with another heuristic method (the time-variant elitist mutation particle swarm optimisation algorithm) and with the standard CSA and particle swarm optimisation methods. Pecsa was found to improve system operation and outperformed the other methods in terms of achieving a faster convergence rate. Pecsa also showed only minor variations in the optimal pattern of release policies compared with those obtained using the standard CSA. The implications of the results and suggestions for further research are discussed.

中文翻译：

---

使用参数精英布谷鸟搜索算法优化水库运行

由于水流的不确定性，水库优化运行已成为一个具有挑战性的问题。水库优化运行框架在水资源管理中发挥着至关重要的作用。有效和明智地利用水库的水有助于管理缺水时期。水库优化的主要目标是设计可用于得出水库释放实时决策的操作规则。在本研究中，基于基本布谷鸟搜索算法 (CSA) 的元启发式优化算法——参数精英布谷鸟搜索算法 (Pecsa)——被应用于为印度 MRP 中的 RSSR 制定最佳操作决策。为了评估 Pecsa 的性能，将获得的结果与另一种启发式方法（时变精英突变粒子群优化算法）以及标准 CSA 和粒子群优化方法进行了比较。人们发现 Pecsa 可以改善系统运行，并且在实现更快的收敛速度方面优于其他方法。Pecsa 还显示，与使用标准 CSA 获得的释放策略的最佳模式相比，只有微小的变化。讨论了结果的含义和对进一步研究的建议。