کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
393543 665655 2011 23 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
چکیده انگلیسی

Differential evolution (DE) is a versatile and efficient evolutionary algorithm for global numerical optimization, which has been widely used in different application fields. However, different strategies have been proposed for the generation of new solutions, and the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, we present two DE variants with adaptive strategy selection: two different techniques, namely Probability Matching and Adaptive Pursuit, are employed in DE to autonomously select the most suitable strategy while solving the problem, according to their recent impact on the optimization process. For the measurement of this impact, four credit assignment methods are assessed, which update the known performance of each strategy in different ways, based on the relative fitness improvement achieved by its recent applications. The performance of the analyzed approaches is evaluated on 22 benchmark functions. Experimental results confirm that they are able to adaptively choose the most suitable strategy for a specific problem in an efficient way. Compared with other state-of-the-art DE variants, better results are obtained on most of the functions in terms of quality of the final solutions and convergence speed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 181, Issue 24, 15 December 2011, Pages 5364–5386
نویسندگان
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