基于非线性规划模型求解的灰狼、乌鸦和鲸鱼算法优化水库大坝运行

H. Ahmadi
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引用次数: 4

摘要

优化水资源运行,特别是在水资源运行中占比最大的农业部门,具有极其重要的意义。因此,本研究在引入鲸鱼、灰狼和乌鸦搜索优化算法的同时,基于可靠性、可逆性和脆弱性指标,对它们在Golestan单库系统大坝优化运行中的性能进行评价,以满足下游土地的用水需求。在此优化问题中,目标函数定义为运行期间总缺额的最小化。同时,将连续方程、溢流约束、库容约束和水库放水量约束应用于问题的目标函数。然后,将结果与基于GAMS软件的非线性规划方法得到的绝对最优值进行比较;最后,建立了一个多准则决策模型,对优化算法的性能进行排序。基于非线性规划方法的GAMS软件得到的绝对最优响应为19.41。结果表明,灰狼算法在优化目标函数方面优于其他算法,灰狼算法、乌鸦搜索算法和鲸鱼搜索算法的平均响应分别为绝对最优响应的92%、84%和67%。此外,灰狼优化算法在所有参数上都优于鲸鱼和乌鸦搜索算法。此外,灰狼算法得到的响应变异系数比鲸鱼和乌鸦搜索算法分别小2倍和1.43倍。最后,多准则决策模型结果表明,在求解Golestan大坝水库优化调度问题时,灰狼算法优于其他两种算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Reservoir Dam Operation Using Gray Wolf, Crow Search and Whale Algorithms Based on the Solution of the Nonlinear Programming Model
Optimizing the water resources operation, especially in the agricultural sector, which has the largest share in the water resources operation, is extremely important. Therefore, in this research, while introducing Whale, Gray Wolf and Crow Search Optimization Algorithms, their performance in the optimum operation of Golestan single-reservoir system Dam was evaluated with the aim of providing water demand for the downstream lands based on reliability, Reversibility, and vulnerability indices. In this optimization problem, the objective function was defined as the minimization of the total deficiency during the operation period. Meanwhile, the constraints of continuity equation, overflow, storage and reservoir release volume were applied to the objective function of the problem. Then, the results were compared with the absolute optimal value based on the nonlinear programming method obtained from GAMS software; finally, a multi-criteria decision-making model was developed to rank the optimization algorithms in terms of performance. The absolute optimal response obtained by the GAMS software based on the nonlinear programming method was 19.41. The results showed that the Gray Wolf algorithm performed better than the other algorithms in optimizing the objective function, so that the average responses in Gray Wolf, Crow Search and Whale algorithms were 92, 84 and 67% of the absolute optimal response, respectively. Furthermore, the Gray Wolf optimization algorithm performs better than the Whale and Crow Search algorithms in all parameters. In addition, the coefficient of variation of the responses obtained by the Gray Wolf algorithm is 2 and 1.43 times smaller than that in the Whale and Crow Search Algorithms, respectively. Finally, the results of the multi-criteria decision-making model showed that the gray wolf algorithm had the first rank, as compared to the other two algorithms studied in solving the problem of the optimal operation of the Golestan dam reservoir.
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