{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:53:53Z","timestamp":1760709233220,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,14]],"date-time":"2017-01-14T00:00:00Z","timestamp":1484352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The State Key Program of National Natural Science of China","award":["51239004"],"award-info":[{"award-number":["51239004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a10010014","type":"journal-article","created":{"date-parts":[[2017,1,16]],"date-time":"2017-01-16T09:44:02Z","timestamp":1484559842000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification"],"prefix":"10.3390","volume":"10","author":[{"given":"Yu","family":"Feng","sequence":"first","affiliation":[{"name":"School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Jianzhong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Muhammad","family":"Tayyab","sequence":"additional","affiliation":[{"name":"School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11047-008-9098-4","article-title":"A survey on metaheuristics for stochastic combinatorial optimization","volume":"8","author":"Bianchi","year":"2009","journal-title":"Nat. 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