Abstract
This research deals with the constrained industrial optimization task, which is the optimization of technological parameters for the waste processing batch reactor. This paper provides a closer insight into the performance of connection between constrained optimization and different strategies of Differential Evolution (DE). Thus, the motivation behind this research is to explore and investigate the differences in performance of basic canonical strategies of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). The simple experiment has been carried out here and 30 times repeated. Consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.
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Acknowledgements
This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by the financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, partially supported by Grant SGS 2017/134 of VSB-Technical University of Ostrava; and by Internal Grant Agency of Tomas Bata University under the projects No. IGA/Cebia-Tech/2017/004.
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Senkerik, R., Viktorin, A., Pluhacek, M., Kadavy, T., Zelinka, I. (2018). Differential Evolution for Constrained Industrial Optimization. In: Duy, V., Dao, T., Zelinka, I., Kim, S., Phuong, T. (eds) AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2017. Lecture Notes in Electrical Engineering, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-69814-4_12
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DOI: https://doi.org/10.1007/978-3-319-69814-4_12
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