Optimal operation of multi-objective reservoirs is one of the complex and, sometimes nonlinear, issues in multi-objective hydrologic system optimization. Meta-heuristic algorithms are good optimization tools which look for decision space via simulating the behavior of animals and providing the possibility for presenting a set of points as a set of problem solutions. Therefore, in this study, developing multi-objective water cycle algorithm (MOWCA) was investigated for the optimal operation issue of Halilrood basin reservoir system (Baft, Safarood, and Jiroft Dams) in order to hydropower energy generation of Jiroft Dam, downstream demand supply (drinking, agricultural, and environmental requirements), and flood control for a period of 223 months (from October 2000 to April 2019). Also, the results of the algorithm were compared with those of the well-known non-dominated sorting genetic algorithm II (NSGA-II). To evaluate the efficiency of the used multi-objective algorithms, four performance evaluation criteria including generational distance (GD), metric of spacing (S), metric of spread (Δ), and maximum spread (MS) were used. The results of applying multi-objective performance evaluation criteria showed the superiority of the developed MOWCA method in three criteria of distance, spread, and maximum spread criteria, while the NSGA-II algorithm was superior to the MOWCA only in metric of spacing (S) criterion. Moreover, the MOWCA algorithm with total 236.07 objectives performed better than the NSGA-II algorithm with total 268.01 objectives. In Jiroft Dam’s hydropower energy generation, the MOWCA algorithm with 4278.69 MW power generation was considerably superior to the NSGA-II algorithm with 3138.55 MW MW generation during the studied period. Finally, the obtained results showed higher performance of the MOWCA algorithm than the NSGA-II algorithm in the optimal operation of Halilrood basin multi-objective reservoirs systems with different objectives.

多目标水库的优化运行是多目标水文系统优化中的复杂问题之一，有时甚至是非线性问题。元启发式算法是很好的优化工具，可以通过模拟动物的行为来寻找决策空间，并提供将一组要点表示为一组问题解决方案的可能性。因此，在本研究中，针对哈利路德盆地水库系统（Baft，Safarood和Jiroft大坝）的最优运行问题，研究了开发多目标水循环算法（MOWCA），以便为Jiroft大坝的水力发电，下游需求供应（饮水，农业和环境要求）和防洪为期223个月（从2000年10月到2019年4月）。还，将该算法的结果与著名的非支配排序遗传算法II（NSGA-II）的结果进行了比较。为了评估使用的多目标算法的效率，使用了四个性能评估标准，包括世代距离（GD），间距度量（S），扩展度量（Δ）和最大扩展（MS）。应用多目标性能评估标准的结果表明，所开发的MOWCA方法在距离，扩展和最大扩展标准这三个标准中均具有优势，而NSGA-II算法仅在间隔度量（S）方面优于MOWCA。标准。此外，总目标为236.07的MOWCA算法的性能要优于总目标为268.01的NSGA-II算法。在Jiroft大坝的水力发电中，采用4278的MOWCA算法。在研究期间，69兆瓦的发电量大大优于NSGA-II算法，发电量为3138.55兆瓦。最后，所获得的结果表明，在具有不同目标的哈利鲁德盆地多目标储层系统的最优运行中，MOWCA算法的性能要优于NSGA-II算法。