## 3 Results and Discussion
In fact, when computational complexity is high, more time and memory are needed to find the best solution. At first glance, computational complexity might not be a key decision factor in selecting an optimization model. However, an optimization model with high computational complexity might take a long time to simulate a long-term period or numerous simulations may be required in a practical project. Figure 12: Vulnerability indices (top: nitrate concentration; bottom: total cultivation area in the catchment). We applied the proposed model to optimize the nitrate concentration. However, this method can be applied for other water quality parameters as well. Analysis of the interaction of several water quality parameters in an aquatic habitat might be complex, which means upgrading the proposed method is necessary. This method can be upgraded to assess the interactions of many water quality parameters for designing an optimal cropping pattern. The economic considerations were a priority in the cultivation pattern optimization in most previous studies. However, the result of the case study indicated that an incorrect cultivation pattern can drastically reduce aquatic populations. A very high concentration of nitrate even in one time step might disrupt the biological activities of various species, which might reduce the population of that species in a river. One of the recommended future research needs is to carry out sensitivity analysis to investigate the impact of each parameter on the optimal cultivation pattern. In our case study, revenue was not reduced dramatically by the optimization of the cultivation pattern. In fact, our assessment demonstrated that the highest possible revenue is USD 2.73 million, which is close to the revenue proposed using the SCE, as the best algorithm, in the present study. We recommend using the proposed method in practical projects for protecting the environmental values of river habitats downstream of agricultural catchments. This study corroborates the outcomes of a previous study regarding the robustness of the SWAT for modeling sustainable agriculture [1]. According to the literature, the use  
Figure 13: Top: Curve convergence of algorithms; bottom: final ranking using the FTOPSIS method. SWAT was validated for ensuring the cost-effectiveness of the best management practices in improving the water quality of agricultural drainage [1]. This study adds an important point to the existing knowledge by applying SWAT in the process of cropping pattern optimization. Hence, SWAT is a useful tool not only for simulating best management practices but also for optimizing cropping patterns. We also need to highlight the limitations of the proposed method. Using the SWAT for water quality simulation needs considerable data, which might not be available in many cases. So, simpler methods/models are recommended in cases studies in which extensive catchment data are not available. Moreover, applying evolutionary optimization might have some limitations. This algorithm is not able to guarantee global optimization, which means the outputs might not be highly reliable in terms of providing an optimal solution. The computational time of the proposed optimization methods is another limitation, which might reduce the attractiveness of the method, particularly for large catchments. Another limitation of this study is the lack of uncertainty analysis, which is a real need in practical projects. It should be noted that the main sources of uncertainty in the proposed method are the reliability of the land use, soil, precipitation, and air temperature data.