Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm

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• 作者：Peifeng Niu^a ; ^b ; Ke Chen^a ; ^b ; ^{chenke_zixf@163.com} ; Yunpeng Ma^a ; ^b ; Xia Li^a ; ^b ; Aling Liu^c ; Guoqiang Li^a ; ^b
• 关键词：Krill herd algorithm ; Fast learning network ; Heat rate ; Steam turbine
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：118
• 期：Complete
• 页码：80-92
• 全文大小：1428 K
• 卷排序：118

文摘

The krill herd (KH) is an innovative biologically-inspired algorithm. To improve the solution quality and to quicken the global convergence speed of KH, an ameliorated krill herd algorithm (A-KH) is proposed to solve the aforementioned problems and test it by classical benchmark functions, which is one of the major contributions of this paper. Compared with other several state-of-art optimization algorithms (biogeography-based optimization, particle swarm optimization, artificial bee colony and krill herd algorithm), A-KH shows better search performance. There is, furthermore, another contribution that the A-KH is adopted to adjust the parameters of the fast learning network (FLN) so as to build the turbine heat rate model of a 600MW supercritical steam and obtain a high-precision prediction model. Experimental results show that, compared with other several turbine heat rate models, the tuned FLN model by A-KH has better regression precision and generalization capability.