Developing Water Cycle Algorithm for Optimal Operation in Multi-reservoirs Hydrologic System

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.