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

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 algor...

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Vydané v:Neural computing & applications Ročník 32; číslo 10; s. 5583 - 5598
Hlavní autori: Liu, Ao, Li, Peng, Sun, Weiliang, Deng, Xudong, Li, Weigang, Zhao, Yuntao, Liu, Bo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.05.2020
Springer Nature B.V
Predmet:
Advances in Parallel and Distributed Computing for Neural Computing
Algorithms
Alloying
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Data Mining and Knowledge Discovery
High strength low alloy steels
Image Processing and Computer Vision
Mechanical properties
Microalloying
Neural networks
Optimization
Probability and Statistics in Computer Science
Searching
Tensile strength
Water waves
Meta-Lamarckian learning
Water wave optimization
Neural networks
Prediction of mechanical properties
ISSN:0941-0643, 1433-3058
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Shrnutí: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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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04149-1