کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6905255 862813 2015 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization
ترجمه فارسی عنوان
تکامل متفرقه جمعیتی با مجموعه ای متعادل از استراتژی های جهش برای بهینه سازی جهانی در مقیاس بزرگ
کلمات کلیدی
تکامل دیفرانسیل، بهینه سازی در مقیاس بزرگ، استراتژی جهش د، مقیاس پذیری،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Differential evolution (DE) is a simple, yet very effective, population-based search technique. However, it is challenging to maintain a balance between exploration and exploitation behaviors of the DE algorithm. In this paper, we boost the population diversity while preserving simplicity by introducing a multi-population DE to solve large-scale global optimization problems. In the proposed algorithm, called mDE-bES, the population is divided into independent subgroups, each with different mutation and update strategies. A novel mutation strategy that uses information from either the best individual or a randomly selected one is used to produce quality solutions to balance exploration and exploitation. Selection of individuals for some of the tested mutation strategies utilizes fitness-based ranks of these individuals. Function evaluations are divided into epochs. At the end of each epoch, individuals between the subgroups are exchanged to facilitate information exchange at a slow pace. The performance of the algorithm is evaluated on a set of 19 large-scale continuous optimization problems. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that mDE-bES has a competitive performance and scalability behavior compared to the contestant algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 33, August 2015, Pages 304-327
نویسندگان
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