دانلود مقاله ISI انگلیسی شماره 137863
ترجمه فارسی عنوان مقاله

مکانیزم انتخاب خودکار الگوریتم های تکامل الگوریتم های مختلف و کاربرد آن

عنوان انگلیسی
Auto-selection mechanism of differential evolution algorithm variants and its application
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
137863 2017 33 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : European Journal of Operational Research, Available online 16 October 2017

ترجمه کلمات کلیدی
محاسبات تکاملی، تکامل دیفرانسیل، برآورد پارامتر، چند الگوریتم انتخاب،
کلمات کلیدی انگلیسی
Evolutionary computations; Differential evolution; Parameter estimation; Multi-algorithm selection;
پیش نمایش مقاله
پیش نمایش مقاله  مکانیزم انتخاب خودکار الگوریتم های تکامل الگوریتم های مختلف و کاربرد آن

چکیده انگلیسی

Each type of problems, such as unimodal/multimodal, linear/non-linear, convex/non-convex, and symmetrical/asymmetrical, has its own characteristics. Although various differential evolution (DE) variants have been proposed, several studies indicate that a DE variant may only exhibit high solution efficiency in solving a specific type of problems, but may perform poorly in others. Therefore, an important decision is made to automatically select a suitable DE variant among several chosen algorithms for solving a particular type of problems during the evolutionary process. To achieve this objective, an auto-selection mechanism (ASM) is introduced in this study. In the ASM, rankings attained using Friedman's test are adopted to assess the performances of DE variants. A learning strategy is employed to update the choice probabilities of DE variants, and an additional selection probability is used to alleviate the greedy selection issue. Three sets of benchmark test functions proposed in BBOB2012, IEEE CEC2005, and IEEE CEC2014 are used to evaluate the effectiveness of the ASM. The performance of the proposed algorithm is also compared with that of nine state-of-the-art DE variants and four non-DE algorithms. Statistical analysis results demonstrate that the ASM is an efficient and effective method that can take full advantages of multiple algorithms. Furthermore, the ASM is utilized to estimate the parameters of a heavy oil thermal cracking model. Experimental results indicate that the proposed algorithm outperforms the other compared algorithms in this case.