Abstract
This research paper combines two soft computing fields – chaos theory and evolutionary computing. The proposed multi-chaotic system implements five different chaotic maps as a Pseudo-Random Number Generators (PRNGs) for parent selection process in Differential Evolution (DE) and Success-History based Adaptive Differential Evolution (SHADE) algorithms. The probabilities for selecting chaotic maps are adapted and the adaptation process is based on the previous successful solutions. Therefore, PRNG varies for different test functions. The performance of multi-chaotic system induced DE and SHADE is compared against their canonical versions on CEC2015 benchmark set. Acquired results show that replacing classic PRNG with multi-chaotic PRNG can lead sto improvement in terms of convergence speed and ability to reach the global optimum.
A. Viktorin—This work was supported by the Programme EEA and Norway Grants for funding via grant on Institutional cooperation project nr. NF-CZ07-ICP-4-345-2016, also 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/2016/007.
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Viktorin, A., Pluhacek, M., Senkerik, R. (2016). Multi-chaotic System Induced Success-History Based Adaptive Differential Evolution. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_44
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