Abstract
Differential Evolution (DE) is a simple and effective evolutionary algorithm to solve optimization problems. The existing DE variants always maintain or increase the randomness of the differential vector when considering the trade-off of randomness and certainty among three components of the mutation operator. This paper considers the possibility to achieve a better trade-off and more accurate result by reducing the randomness of the differential vector, and designs a tight adaptive DE variant called TADE. In TADE, the population is divided into a major subpopulation adopting the general “current-to-pbest” strategy and a minor subpopulation utilizing our proposed strategy of sharing the same base vector but reducing the randomness in differential vector. Based on success-history parameter adaptation, TADE designs a simple information exchange scheme to avoid the homogeneity of parameters. The extensive experiments on CEC2014 suite show that TADE achieves better or equivalent performance on at least 76.7 % functions comparing with five state-of-the-art DE variants. Additional experiments are conducted to verify the rationality of this tight design.
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Acknowledgments
This work is supported in part by National Natural Science Foundation of China (Grant Nos. 61303003, 41374113), by Tsinghua University Initiative Scientific Research Program (Grant No. 20131089356).
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Zheng, W., Fu, H., Yang, G. (2016). TADE: Tight Adaptive Differential Evolution. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_11
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DOI: https://doi.org/10.1007/978-3-319-45823-6_11
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