کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4942745 | 1437416 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Constrained gravitational search algorithm for large scale reservoir operation optimization problem
ترجمه فارسی عنوان
الگوریتم جستجو گرانشی محدود برای مسئله بهینه سازی عملیات مخزن در مقیاس بزرگ
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کلمات کلیدی
الگوریتم تکاملی، عملکرد مطلوب مخزن، الگوریتم جستجوی گرانشی، به طور صریح محدودیت رسیدگی، آب آزاد می شود حجم ذخیره سازی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
The gravitational search algorithm (GSA) is used in this paper to solve large scale reservoir operation optimization problem. Here, two constrained versions of GSA are proposed to solve this problem in which masses may be forced to satisfy problem constraints during solution building. This approach is very useful when attempting to solve large scale optimization problem as it will lead to a considerable reduction of the search space size. Here, in the second version of GSA, the storage volume bounds of the reservoir are modified prior to the main search to recognize the infeasible components of the search space and exclude from the search process before the main search starts. Two formulations are also proposed here for each proposed algorithm considering water releases or storage volumes at each operation time period as decision variable of the problem. Proposed algorithms are used to solve the simple and hydropower operation problem of “Dez” reservoir in Iran and the results are presented and compared with using original form of the GSA and any available results. The results indicate the ability of the proposed algorithm and especially the second constrained version of GSA to optimally solve the reservoir operation optimization problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 62, June 2017, Pages 222-233
Journal: Engineering Applications of Artificial Intelligence - Volume 62, June 2017, Pages 222-233
نویسندگان
R. Moeini, M. Soltani-nezhad, M. Daei,