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

  • Advances in Parallel and Distributed Computing for Neural Computing
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Abstract

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.

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Acknowledgements

The authors would like to thank the anonymous referees for their constructive and valuable comments on the earlier manuscript of this paper. This research is partially supported by National Natural Science Foundation of China under Grant Nos. 71701156 and 51774219, Key Research Program of Frontier Sciences for Chinese Academy of Sciences under Grant No. QYZDB-SSW-SYS020, Natural Science Foundation of Hubei Province of China under Grant No. 2017CFB427, Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant No. 16YJCZH056.

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Authors and Affiliations

  1. School of Management, Wuhan University of Science and Technology, Wuhan, 430065, China

    Ao Liu & Xudong Deng

  2. Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430065, China

    Ao Liu & Xudong Deng

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Peng Li & Bo Liu

  4. School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China

    Weiliang Sun

  5. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Weigang Li & Yuntao Zhao

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  1. Ao Liu
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Correspondence to Bo Liu.

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This paper is an extended version of our original paper titled “Designing Neural Networks Using Novel Water Wave Optimization-Based Memetic Algorithm” which was accepted by the conference ICNC-FSKD 2018 held from 28 to 30 July 2018 in Huangshan, China, and was recommended to the special issue of Advances in Parallel and Distributed Computing for Neural Computing by Prof. Jianguo Chen. We strictly comply with the provisions that: (1) extend the paper at least 40% by enhancing the sections of introduction and description of algorithm. Furthermore, a novel application on the prediction of the mechanical properties of micro-alloyed steels was added in the paper; (2) reformat the paper in terms of the requirements of Neural Computing and Applications.

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Liu, A., Li, P., Sun, W. et al. Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization. Neural Comput & Applic 32, 5583–5598 (2020). https://doi.org/10.1007/s00521-019-04149-1

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