Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization

doi:10.1007/s00521-019-04149-1

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	Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization
	Liu, Ao 1,2; Li, Peng 3; Sun, Weiliang 4; Deng, Xudong 1,2; Li, Weigang 5; Zhao, Yuntao 5; Liu, Bo 3
	2020-05-01
发表期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
卷号	32 期号:10 页码:5583-5598
摘要	Searching optimal parameters for neural networks can be formulated as a multi-modal optimization problem. This paper proposes a novel water wave optimization (WWO)-based memetic algorithm to identify the optimal weights for neural networks. In the proposed water wave optimization-based memetic algorithm (WWOMA), we employ WWO to perform global search by both individual improvement and population co-evolution and then employ several local search components to enhance its local refinement ability. Moreover, an effective Meta-Lamarckian learning strategy is utilized to choose a proper local search component to concentrate computational efforts on more promising solutions. We carry out simulation experiments on six well-known neural network designing benchmark problems, both the simulation results and statistical comparisons demonstrate the feasibility, effectiveness and efficiency of applying WWOMA to design neural networks. Furthermore, we apply WWOMA to design neural networks and use well-trained neural networks to predict tensile strength of micro-alloyed steels. Evaluation on a practical industrial case with 2489 sample data shows that, in comparison with other algorithms, WWOMA-based neural networks can obtain notable and robust prediction accuracy, which further demonstrates that WWOMA is a promising and efficient algorithm for designing neural networks. It is worth mentioning that, to the best of our knowledge, this is the first report about applying water wave optimization to train neural networks.
关键词	Neural networks Water wave optimization Meta-Lamarckian learning Prediction of mechanical properties
DOI	10.1007/s00521-019-04149-1
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000529745200024
出版者	SPRINGER LONDON LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/51400
专题	中国科学院数学与系统科学研究院
通讯作者	Liu, Bo
作者单位	1.Wuhan Univ Sci & Technol, Sch Management, Wuhan 430065, Peoples R China 2.Wuhan Univ Sci & Technol, Ctr Serv Sci & Engn, Wuhan 430065, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300350, Peoples R China 5.Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan 430081, Peoples R China
推荐引用方式 GB/T 7714	Liu, Ao,Li, Peng,Sun, Weiliang,et al. Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization[J]. NEURAL COMPUTING & APPLICATIONS,2020,32(10):5583-5598.
APA	Liu, Ao.,Li, Peng.,Sun, Weiliang.,Deng, Xudong.,Li, Weigang.,...&Liu, Bo.(2020).Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization.NEURAL COMPUTING & APPLICATIONS,32(10),5583-5598.
MLA	Liu, Ao,et al."Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization".NEURAL COMPUTING & APPLICATIONS 32.10(2020):5583-5598.