کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
425742 | 685839 | 2006 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
PGGA: A predictable and grouped genetic algorithm for job scheduling
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: PGGA: A predictable and grouped genetic algorithm for job scheduling PGGA: A predictable and grouped genetic algorithm for job scheduling](/preview/png/425742.png)
چکیده انگلیسی
This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is two-fold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records and (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods, such as first-come-first-serve (FCFS) and random scheduling, heuristics, such as a typical genetic algorithm, Min–Min and Max–Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 22, Issue 5, April 2006, Pages 588–599
Journal: Future Generation Computer Systems - Volume 22, Issue 5, April 2006, Pages 588–599
نویسندگان
Maozhen Li, Bin Yu, Man Qi,