کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
494539 862799 2016 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Enhanced artificial bee colony algorithm through differential evolution
ترجمه فارسی عنوان
الگوریتم کلونی زنبور عسل از طریق تکامل تفاضلی
کلمات کلیدی
الگوریتم کلونی زنبور عسل مصنوعی؛ تکامل تفاضلی؛ مقداردهی اولیه جمعیت؛ استراتژی ارزیابی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• When producing the initial population, the chaotic opposition-based population initialization method is employed to enhance the global convergence.
• A learning strategy is developed with an attempt to use more prior information of previous search experience.
• A hybrid approach that combines DE with gbest-guided ABC, is designed to improve the performance of ABC.
• The experiment results demonstrate the good performance of the proposed algorithm.

Artificial bee colony algorithm (ABC) is a relatively new optimization algorithm. However, ABC does well in exploration but badly in exploitation. One possible way to improve the exploitation ability of the algorithm is to combine ABC with other operations. Differential evolution (DE) can be considered as a good choice for this purpose. Based on this consideration, we propose a new algorithm, i.e. DGABC, which combines DE with gbest-guided ABC (GABC) by an evaluation strategy with an attempt to utilize more prior information of the previous search experience to speed up the convergence. In addition, to improve the global convergence, when producing the initial population, a chaotic opposition-based population initialization method is employed. The comparison results on a set of 27 benchmark functions demonstrate that the proposed method has better performance than the other algorithms.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 48, November 2016, Pages 137–150
نویسندگان
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