Abstract
This research analyzes the current archive of inferior solutions used in Success-History based Adaptive Differential Evolution (SHADE) and tests its influence on the result of optimization. A novel Enhanced Archive (EA) is analyzed in the same way and the results are compared. In order to compare both methods on different types of test functions, CEC2015 benchmark set was used as the test bed. Results suggest that there is a possibility for further research because current existing archive implementations do not provide sufficient benefits to the optimization algorithm.
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Acknowledgements
This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2017/004.
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Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T. (2017). The Influence of Archive Size to SHADE. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_51
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DOI: https://doi.org/10.1007/978-3-319-57261-1_51
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