کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11021178 | 1715032 | 2019 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A scalarization-based dominance evolutionary algorithm for many-objective optimization
ترجمه فارسی عنوان
الگوریتم تکاملی غالب بر اساس مقیاس سازی برای بهینه سازی چند هدف
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Classical Pareto-dominance based multi-objective evolutionary algorithms underperform when applied to optimization problems with more than three objectives. A class of multi-objective evolutionary algorithms introduced in the literature, utilizing pre-determined reference points acting as target vectors to maintain diversity in the objective space, has shown promising results. Inspired by this approach, we propose a scalarization-based dominance evolutionary algorithm (SDEA) that utilizes a reference point-based method and combine it with a novel sorting strategy that employs fitness values determined via scalarization. SDEA reduces computation complexity by eliminating the need for a Pareto-dominance approach to obtain non-dominated solutions. By means of a set of common benchmark optimization problems with 3- to 15-objectives, we compare the performance of SDEA with state-of-the-art many-objective evolutionary algorithms. The results indicate that SDEA outperforms existing algorithms in undertaking complex optimization problems with a high number of objectives, and has comparable outcomes over low-dimensional objective space benchmark problems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 474, February 2019, Pages 236-252
Journal: Information Sciences - Volume 474, February 2019, Pages 236-252
نویسندگان
Burhan Khan, Samer Hanoun, Michael Johnstone, Chee Peng Lim, Douglas Creighton, Saeid Nahavandi,