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State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions | IEEE Journals & Magazine | IEEE Xplore

State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions


Steps of our method.

Abstract:

Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained...Show More

Abstract:

Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree.
Steps of our method.
Published in: IEEE Access ( Volume: 6)
Page(s): 76586 - 76595
Date of Publication: 21 November 2018
Electronic ISSN: 2169-3536

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