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کاربرد الگوریتم نوین بهینه‌سازی گروه گوریل‌ها برای مدیریت بهره‌برداری مخزن | ||
| تحقیقات آب و خاک ایران | ||
| دوره 52، شماره 11، بهمن 1400، صفحه 2829-2842 اصل مقاله (1.77 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2021.332658.669106 | ||
| نویسندگان | ||
| ایرج پسندیده1؛ بهروز یعقوبی* 2 | ||
| 1دانشجوی دکتری منابع آب، گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران | ||
| 2گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران | ||
| چکیده | ||
| در این مقاله الگوریتم فرا ابتکاری بهینه‌ساز گروه گوریل‌های مصنوعی (GTO) با الگوریتم‌های بهینه‌ساز گرگ خاکستری (GWO) و ازدحام ذرات (PSO) به منظور مدیریت بهره‌برداری بهینه از مخزن سد مقایسه شد. نتایج الگوریتم GTO با الگوریتم‌های GWO و PSO که در زمینه مسائل پیچیده مهندسی و بهره‌برداری از مخزن موفق عمل کردند، ارزیابی شد. تابع هدف کمینه‌سازی مجموع مربعات کمبود نیاز پایین‌دست طی دوره بهره‌برداری تعریف شد و قیود مربوط به معادله پیوستگی مخزن، حجم مخزن و حجم رهاسازی بر آن اعمال گردید. مطالعه موردی سد مخزنی جامیشان واقع در استان کرمانشاه در نظر گرفته شد. در این راستا مقادیر رواناب مربوط به سال‌های آماری 1390-1370 بعنوان جریان ورودی آب به مخزن برای مدیریت بهره‌برداری بهینه از مخزن معرفی شد. نتایج بدست آمده از الگوریتم‌های بهینه‌سازی با استفاده از شاخص‌های ارزیابی خطا شامل جذر میانگین مربعات خطا (RMSE)، میانگین قدر مطلق خطا (MAE)، معیار نش-ساتکلیف (NSE)، نسبت جذر میانگین مربعات خطا به انحراف معیار داده‌های مشاهداتی (RSR)، اطمینان‌پذیری، برگشت‌پذیری، آسیب‌پذیری و کمینه‌سازی تابع هدف مورد ارزیابی قرار گرفتند. مقادیر این شاخص‌ها به ترتیب برای GTO 86/2، 85/1، 73/0، 52/0، 69%، 36%، 23% و 7/4 می‌باشند. مقادیر این شاخص‌ها مشخص کردند الگوریتم GTO دارای دقت بسیار خوبی بوده و بهتر از الگوریتم‌های GWO و PSO عمل میکند. لذا الگوریتم GTO می‌تواند بعنوان یک الگوریتم قدرتمند برای حل مسائل بهره‌برداری بهینه از مخزن سد بکار رود. براساس این الگوریتم، مقدار حجم رهاسازی آب بصورت تابعی از حجم مخزن سد، آورد به مخزن و مقادیر حجم تقاضای آب برای ماه‌های سال تعیین گردید. | ||
| کلیدواژه‌ها | ||
| بهینه‌سازی؛ الگوریتم فرا ابتکاری؛ گروه گوریل‌های مصنوعی؛ بهره‌برداری از مخزن | ||
| عنوان مقاله [English] | ||
| Application of a New Gorilla Troops Optimization Algorithm for Reservoir Operation Management | ||
| نویسندگان [English] | ||
| iraj pasandideh1؛ Behrouz Yaghoubi2 | ||
| 1Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| 2Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| چکیده [English] | ||
| In this paper, the artificial gorilla troops (GTO) optimizer algorithm with the gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms was compared for optimal operation of dam reservoir. The results of the GTO were evaluated with GWO and PSO results which are successful in complex engineering issues and reservoir operation. The objective function of minimizing the total squares of the downstream demand deficit was defined during the operation period. The constraints of the operating equation include the reservoir continuity equation, the reservoir volume, and the release volume. A case study of Jamishan reservoir dam located in Kermanshah province was considered. In this regard, runoff values related to the statistical years 1991-2011 were introduced as input to the reservoir for optimal operation management. The obtained results from optimization algorithms using error estimation indices including mean square root of error (RMSE), mean absolute value of error (MAE), Nash-Sutcliffe criterion (NSE), ratio of root mean square error to standard deviation of observational data (RSR), Reliability, Resiliency, Vulnerability And the minimization values of the objective function were evaluated. The values of these indicators for GTO were 2.86, 1.85, 0.73, 0.52, 69%, 36%, 23% and 4.7, respectively. The results showed that the GTO algorithm had very good accuracy in minimizing the objective function and based on the values of proposed indicators performed better than the GWO and PSO algorithms. Based on this algorithm, the amount of water release volume was brought to the reservoir as a function of the reservoir volume and the amount of water demand volume for the months of the year was determined. | ||
| کلیدواژه‌ها [English] | ||
| Optimization, Meta-heuristics algorithm, Artificial gorilla troops optimizer, Reservoir operation | ||
| مراجع | ||
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