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Digital
Library of the European Council for Modelling and Simulation |
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Title: |
Differential Evolution Algorithm In Models Of Technical Optimization |
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Authors: |
Roman Knobloch, Jaroslav Mlynek |
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Published in:
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(2021). ECMS 2021,
35th Proceedings DOI: http://doi.org/10.7148/2021 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) ISBN: 978-3-937436-72-2 Communications of the ECMS , Volume 35, Issue 1, June 2021, United Kingdom |
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Citation
format: |
Roman Knobloch,
Jaroslav Mlynek (2021). Differential
Evolution Algorithm In Models Of Technical Optimization, ECMS 2021 Proceedings Edited By: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela European Council for Modeling and Simulation. doi: 10.7148/2021-0179 |
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DOI: |
https://doi.org/10.7148/2021-0179 |
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Abstract: |
At present, evolutionary
optimization algorithms are increasingly used in the development of new
technological processes. Evolutionary algorithms often allow the optimization
procedure to be performed even in cases where classical optimization
algorithms fail (e.g. gradient methods) and where an acceptable solution is
sufficient to solve the optimization task. The article focuses on possibilities
of using a differential evolution algorithm in the optimization process. This
algorithm is often referred to in the literature as a global optimization
procedure. However, we show by means of a practical example that the
convergence of the classic differential algorithm to the global extreme is not
generally assured and is largely dependent on the specific cost function. To remove
this weakness, we designed a modified version of the differential evolution
algorithm. The improved version, named the modified differential evolution
algorithm, is described in the article. It is possible to prove asymptotic
convergence to the global minimum of the cost function for the modified
version of the algorithm. |
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