کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
476501 699624 2013 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Real parameter optimization by an effective differential evolution algorithm
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
Real parameter optimization by an effective differential evolution algorithm
چکیده انگلیسی

This paper introduces an Effective Differential Evolution (EDE) algorithm for solving real parameter optimization problems over continuous domain. The proposed algorithm proposes a new mutation rule based on the best and the worst individuals among the entire population of a particular generation. The mutation rule is combined with the basic mutation strategy through a linear decreasing probability rule. The proposed mutation rule is shown to promote local search capability of the basic DE and to make it faster. Furthermore, a random mutation scheme and a modified Breeder Genetic Algorithm (BGA) mutation scheme are merged to avoid stagnation and/or premature convergence. Additionally, the scaling factor and crossover of DE are introduced as uniform random numbers to enrich the search behavior and to enhance the diversity of the population. The effectiveness and benefits of the proposed modifications used in EDE has been experimentally investigated. Numerical experiments on a set of bound-constrained problems have shown that the new approach is efficient, effective and robust. The comparison results between the EDE and several classical differential evolution methods and state-of-the-art parameter adaptive differential evolution variants indicate that the proposed EDE algorithm is competitive with , and in some cases superior to, other algorithms in terms of final solution quality, efficiency, convergence rate, and robustness.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Egyptian Informatics Journal - Volume 14, Issue 1, March 2013, Pages 37–53
نویسندگان
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