مدل شبیه سازی سطح آبیاری بهینه سازی بر اساس الگوریتم های فراشناختی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|89975||2018||12 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Agricultural Water Management, Volume 201, 31 March 2018, Pages 46-57
Simulation-optimization models are valuable tools for determining the optimal performance of systems. The main objective of this research was to develop and validate EDOSIM (Evaluation, Design, and Optimization of Surface Irrigation Model) as a simulation-optimization model for surface irrigation systems. For simulation, which consisted of the design or evaluation of basin, border and furrow irrigation, the Volume Balance model was used. For optimization, twenty meta-heuristic algorithms were applied. In this model, based on irrigation, the volume of infiltrated water to soil was calculated without having advance and recession data. The hydraulic objective function was used to minimize the linear combination of seven performance indicators. Regarding the optimization of the objective function, the functional, multi-dimensional, static, constraint, continuous, single-objective, and meta-heuristic optimizations were applied. Data obtained from fifteen experimental fields were used for the validation of simulation, algorithms parameters setting, and validation of optimization. Comparison of the simulation results of the EDOSIM model with those of the Hydrodynamic model of SIRMOD software showed the good performance of EDOSIM model and the proposed method for estimating the volume of infiltration with RMSEâ¯=â¯0.068, R2â¯=â¯0.988, CRMâ¯=â¯0.005 and NRMSEâ¯=â¯4.2%. The Shuffled Complex Evolution (SCE) algorithm was found to be the best algorithm for the optimization of fields; in all fields, the objective function was decreased (improved). Comparison of the objective function of the EDOSIM model with eight solvers of Optimization and Global Optimization Toolboxes of MATLAB software also revealed the superiority of the EDOSIM model for optimization.