کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6857212 | 661905 | 2016 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation
ترجمه فارسی عنوان
الگوریتم تکاملی چند هدفه خود سازگار بر اساس تجزیه برای مشکلات بزرگ مقیاس: یک مطالعه موردی در مورد کنترل سیل مخزن
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کلمات کلیدی
مشکل بهینه سازی چند منظوره در مقیاس بزرگ، انتخاب اپراتور ژنتیکی، انتخاب اندازه محله، عملیات کنترل سیل مخزن،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Large-scale multi-objective optimization problems (LS-MOP) are complex problems with a large number of decision variables. Due to its high-dimensional decision space, LS-MOP poses a significant challenge to multi-objective optimization methods including multi-objective evolutionary algorithms (MOEAs). Following the algorithmic framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an enhanced algorithm with adaptive neighborhood size and genetic operator selection, named self-adaptive MOEA/D (SaMOEA/D), is developed for solving LS-MOP in this work. Learning from the search history, each scalar optimization subproblem in SaMOEA/D varies its neighborhood size and selects a genetic operator adaptively. The former determines the size of the search scope, while the latter determines the search behavior and as a result the newly generated solution. Experimental results on 20 LS-MOP benchmarks have demonstrated that SaMOEA/D outperforms or performs similarly to the other four state-of-the-art MOEAs. The effectiveness of the self-adaptive strategies has also been experimentally verified. Furthermore, SaMOEA/D and the comparing algorithms are then applied to solve a challenging real-world problem, the multi-objective reservoir flood control operation problem. Optimization results illustrate the superiority of SaMOEA/D.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 367â368, 1 November 2016, Pages 529-549
Journal: Information Sciences - Volumes 367â368, 1 November 2016, Pages 529-549
نویسندگان
Yutao Qi, Liang Bao, Xiaoliang Ma, Qiguang Miao, Xiaodong Li,