کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6892755 | 699336 | 2016 | 43 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A comparative study on multiobjective metaheuristics for solving constrained in-core fuel management optimisation problems
ترجمه فارسی عنوان
یک مطالعه تطبیقی در مورد متابولیسم چند هدفه برای حل مشکلات بهینه سازی مدیریت سوخت هسته ای محدود
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
متهوریستی، بهینه سازی چند منظوره، دست زدن به محدودیت، بهینه سازی مدیریت سوخت هسته، راکتور هسته ای،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and state-of-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to determine which approach is the most suitable in the context of constrained MICFMO. A test suite of 16 optimisation problem instances, based on the SAFARI-1 nuclear research reactor, has been established for the comparative study. The suite is partitioned into three classes, each consisting of problem instances having a different number of objectives, but subject to the same stringent constraint set. The effectiveness of a multiplicative penalty function constraint handling technique is also compared with the constrained-domination technique from the literature. The different optimisation approaches are compared in a nonparametric statistical analysis. The analysis reveals that multiplicative penalty function constraint handling is a competitive alternative to constrained-domination, and seems to be particularly effective in the context of bi-objective optimisation problems. In terms of the metaheuristic solution comparison, it is found that the nondominated sorting genetic algorithm II (NSGA-II), the Pareto ant colony optimisation (P-ACO) algorithm and the multiobjective optimisation using cross-entropy method (MOOCEM) are generally the best-performing metaheuristics across all three problem classes, along with the multiobjective variable neighbourhood search (MOVNS) in the bi-objective problem class. Furthermore, the practical relevance of the metaheuristic results is demonstrated by comparing the solutions thus obtained to the current SAFARI-1 reload configuration design approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Operations Research - Volume 75, November 2016, Pages 174-190
Journal: Computers & Operations Research - Volume 75, November 2016, Pages 174-190
نویسندگان
E.B. Schlünz, P.M. Bokov, J.H. van Vuuren,