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Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification

School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Academic Editor: Javier Del Ser Lorente
Algorithms 2017, 10(1), 14; https://doi.org/10.3390/a10010014
Received: 8 October 2016 / Revised: 22 December 2016 / Accepted: 11 January 2017 / Published: 14 January 2017
This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems. View Full-Text
Keywords: kernel fuzzy clustering; differential harmony search; water diversion scheduling kernel fuzzy clustering; differential harmony search; water diversion scheduling
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Feng, Y.; Zhou, J.; Tayyab, M. Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification. Algorithms 2017, 10, 14.

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