کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6874378 | 1441159 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Enhancing differential evolution with random neighbors based strategy
ترجمه فارسی عنوان
افزایش تفاضل دیفرانسیل با استراتژی مبتنی بر همسایگان تصادفی
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
تکامل دیفرانسیل، بهینه سازی جهانی، همسایه تصادفی اکتشاف و استثمار،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
چکیده انگلیسی
As a powerful evolutionary algorithm for solving the tough global optimization problems, differential evolution (DE) has drawn more and more attention. However, how to make a proper balance between the global and local search is a perplexing question and greatly limit the optimization performance of DE. As we all known, there are two classical mutation strategies in DE, i.e., DE/rand/1 and DE/best/1. In DE/rand/1 strategy, the base vector is chosen from the population randomly, this means its better exploration and poor exploitation. The base vector of DE/best/1 strategy is the best one of the population and the strategy has better exploitation and poor exploration. To overcome these problems, this paper proposed a random neighbor based mutation strategy (DE/neighbor/1). For each individual of the population at each generation, the neighbors are chosen from the population in a random manner. The base vector of DE/neighbor/1 mutation strategy is the best one in the neighbors. On the basis of the new strategy, an enhancing differential evolution with DE/neighbor/1 (RNDE) is proposed. The experimental studies have been tested on 27 widely used benchmark functions and the results have proved that the proposed algorithm is competitive and promising.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational Science - Volume 26, May 2018, Pages 501-511
Journal: Journal of Computational Science - Volume 26, May 2018, Pages 501-511
نویسندگان
Hu Peng, Zhaolu Guo, Changshou Deng, Zhijian Wu,