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 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Springer London
01.05.2020
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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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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| AbstractList | 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. |
| Author | Liu, Ao Li, Weigang Liu, Bo Deng, Xudong Li, Peng Sun, Weiliang Zhao, Yuntao |
| Author_xml | – sequence: 1 givenname: Ao surname: Liu fullname: Liu, Ao organization: School of Management, Wuhan University of Science and Technology, Center for Service Science and Engineering, Wuhan University of Science and Technology – sequence: 2 givenname: Peng surname: Li fullname: Li, Peng organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences – sequence: 3 givenname: Weiliang surname: Sun fullname: Sun, Weiliang organization: School of Chemical Engineering and Technology, Tianjin University – sequence: 4 givenname: Xudong surname: Deng fullname: Deng, Xudong organization: School of Management, Wuhan University of Science and Technology, Center for Service Science and Engineering, Wuhan University of Science and Technology – sequence: 5 givenname: Weigang surname: Li fullname: Li, Weigang organization: School of Information Science and Engineering, Wuhan University of Science and Technology – sequence: 6 givenname: Yuntao surname: Zhao fullname: Zhao, Yuntao organization: School of Information Science and Engineering, Wuhan University of Science and Technology – sequence: 7 givenname: Bo surname: Liu fullname: Liu, Bo email: bliu@amss.ac.cn organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences |
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| Cites_doi | 10.1038/s42256-018-0006-z 10.1109/TCBB.2018.2868088 10.1023/A:1021251113462 10.1109/TPDS.2018.2871189 10.1007/s00521-016-2293-9 10.1016/j.ins.2018.06.045 10.1016/j.msea.2017.01.023 10.1080/0305215X.2011.652103 10.1109/ICCA.2017.8003085 10.1109/ICENCO.2015.7416344 10.1109/TSMCB.2012.2213808 10.1016/j.cor.2014.10.008 10.1109/ICCES.2017.8275392 10.1016/j.chaos.2006.05.070 10.1109/CEC.2016.7743990 10.1023/A:1008202821328 10.1023/A:1022602019183 10.1007/s00500-015-1786-2 10.1016/j.ins.2016.12.024 10.1016/j.matdes.2011.10.011 10.1007/s00521-017-2906-y 10.1016/j.engappai.2016.10.009 10.1109/FSKD.2018.8686939 10.1016/j.neucom.2006.10.018 10.1007/s00521-011-0741-0 10.1007/s00521-017-2844-8 10.1109/CEC.2016.7744029 10.1007/s00170-015-8039-5 10.1007/s00521-016-2714-9 10.1002/srin.201100189 10.1109/TSMC.2015.2416127 10.1109/TPDS.2018.2877359 10.1016/j.swevo.2017.06.001 10.1016/j.matdes.2017.05.027 10.1016/j.patcog.2017.06.031 10.1016/j.knosys.2016.06.019 10.1109/ICNN.1995.488968 10.1007/s00521-013-1346-6 10.1007/s10479-011-0894-3 10.1007/s00521-016-2510-6 10.1080/10426914.2014.984203 10.1016/j.ins.2018.01.041 10.1021/ie4000954 10.1016/j.ins.2018.01.001 10.1109/TSMCB.2006.883272 10.1109/TPDS.2016.2603511 10.1016/j.asoc.2018.05.023 10.1016/j.msea.2017.09.039 10.1080/03019233.2016.1227025 10.1109/TEVC.2016.2555315 10.1007/s11063-017-9733-0 10.1080/10426910701323607 10.1109/TEVC.2003.819944 10.1109/TNNLS.2016.2609437 10.1016/j.jpdc.2017.09.006 10.1109/CEC.2016.7744111 10.1016/j.trb.2016.09.006 10.1109/ICCSE.2018.8468778 10.1007/978-3-642-13495-1_44 10.1109/72.572107 10.1007/978-3-540-37258-5_149 10.1007/978-3-540-72395-0_28 10.21037/jtd.2018.02.57 10.1109/MC.2013.6608208 10.1504/IJBIC.2016.074630 |
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| Keywords | Meta-Lamarckian learning Water wave optimization Neural networks Prediction of mechanical properties |
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| References | Unluturk, Unluturk, Pazir, Kuscu (CR31) 2014; 24 Zhang, Gong, Deng, Wang (CR43) 2017; 685 Shi, Chen, Heng, Zhang (CR59) 2018; 30 Goldberg, Holland (CR11) 1988; 3 Wang, Wang, Zhou, Sun, Zhao, Yu, Cui (CR18) 2017; 382 Liu, Wang, Liu, Wang (CR10) 2011; 186 CR36 Qu, Zhang, Liu, Li (CR7) 2017; 57 Engel (CR15) 1988; 2 Wang, Jin, Jansen (CR60) 2016; 20 Kapanova, Dimov, Sellier (CR38) 2018; 29 CR2 Song, Zheng, Huang, Xu, Sheng, Yang (CR8) 2018; 5 Jha, Pettersson, Dulikravich, Saxen, Chakraborti (CR45) 2015; 30 CR4 Kaushal, Khehra, Sharma (CR23) 2018; 70 Chen, Li, Zhuo, Bilal, Yu, Weng, Li (CR61) 2017; 28 CR3 Chen, Qin, Wan, Wang, Liu (CR70) 2015; 35 Alejandro, Raúl, Félix, Valery, David (CR58) 2018; 117 CR48 Donate, Li, Sánchez, Miguel (CR62) 2013; 22 Yao, Liu (CR34) 1997; 8 García-Lamont, Cervantes, López-Chau (CR55) 2018; 30 Liu, Feng, Deng, Ren, Liu (CR5) 2018; 38 Ganguly, Datta, Chakraborti (CR46) 2007; 22 Rao, Savsani, Balic (CR21) 2012; 44 Chen, Li, Bilal, Metwally, Li, Yu (CR64) 2018 Zheng, Sheng, Sun, Chen (CR67) 2017; 28 Zhang, Li, Zhang, Qin, Feng, Liu (CR9) 2016; 20 Fukasawa, He, Song (CR69) 2016; 94 Sui, Lv (CR41) 2016; 85 Yang, Feng, Xin, Ji, Du, Wang, Zhang, Liu (CR73) 2018 Wang, Wang, Cui, Zhou, Zhao, Li (CR6) 2018; 438 Storn, Price (CR13) 1997; 11 Dawidowicz (CR32) 2018; 30 CR17 Liu, Jia, Kong, Feng, Zhang, Zhang (CR40) 2017; 707 Shafiee, Mishra, Wong (CR53) 2018; 48 CR57 CR12 Čiripová, Hryha, Dudrová, Výrostková (CR42) 2012; 35 CR52 Wang, Wang, Sun, Shahryar (CR19) 2016; 8 CR51 Zheng (CR20) 2015; 55 Hore, Das, Banerjee, Mukherjee (CR39) 2017; 44 Stanley, Clune, Lehman, Miikkulainen (CR54) 2019; 1 Antipov, Baccouche, Berrani, Dugelay (CR56) 2017; 72 Ong, Keane (CR49) 2004; 8 Wang, Liu, Liu, Liu (CR71) 2016; 36 Shafaei, Kisi (CR33) 2017; 28 Ho, Pepyne (CR50) 2002; 115 Wang, Rahnamayan, Sun, Omran (CR14) 2013; 43 Yang, Feng, Chi, Li, Duan, Liu, Liang, Wang, Chen, He, Xing (CR66) 2018; 10 Wang, Zheng (CR74) 2018; 38 CR29 CR28 CR27 Wang, Feng, Lyu, Li, Liu (CR22) 2016; 107 CR26 CR25 CR24 Kumar, Chakrabarti, Chakraborti (CR44) 2012; 83 Wang, Wang (CR72) 2016; 46 Mirjalili, Hashim, Sardroudi (CR37) 2012; 218 CR63 Ye, Qiao, Li, Ruan (CR35) 2007; 70 Chen, Li, Rong, Bilal, Yang, Li (CR65) 2018; 435 Liu, Wang, Jin (CR1) 2007; 37 Liu, Jia, Kong, Guan, Zhang (CR47) 2017; 129 Cartwright, Curteanu (CR30) 2013; 52 Pan, Wang, Liu (CR16) 2008; 35 Chen, Li, Yang, Xiao, Xie, Li (CR68) 2018 L Čiripová (4149_CR42) 2012; 35 JG Chen (4149_CR64) 2018 R Storn (4149_CR13) 1997; 11 S Ganguly (4149_CR46) 2007; 22 S Unluturk (4149_CR31) 2014; 24 4149_CR36 M Shafaei (4149_CR33) 2017; 28 M Kaushal (4149_CR23) 2018; 70 H Wang (4149_CR6) 2018; 438 CZ Zhang (4149_CR43) 2017; 685 T Chen (4149_CR70) 2015; 35 R Jha (4149_CR45) 2015; 30 H Cartwright (4149_CR30) 2013; 52 H Wang (4149_CR14) 2013; 43 JP Donate (4149_CR62) 2013; 22 YX Yang (4149_CR73) 2018 4149_CR63 4149_CR24 Q Song (4149_CR8) 2018; 5 4149_CR25 YJ Zheng (4149_CR67) 2017; 28 B Liu (4149_CR1) 2007; 37 4149_CR28 4149_CR29 4149_CR26 4149_CR27 SY Wang (4149_CR72) 2016; 46 H Pan (4149_CR16) 2008; 35 HJ Zhang (4149_CR9) 2016; 20 YD Chen (4149_CR68) 2018 YD Wang (4149_CR22) 2016; 107 XH Qu (4149_CR7) 2017; 57 JG Chen (4149_CR61) 2017; 28 JG Chen (4149_CR65) 2018; 435 YS Ong (4149_CR49) 2004; 8 4149_CR51 J Engel (4149_CR15) 1988; 2 4149_CR52 4149_CR57 4149_CR12 KG Kapanova (4149_CR38) 2018; 29 4149_CR17 DJ Wang (4149_CR71) 2016; 36 F García-Lamont (4149_CR55) 2018; 30 J Dawidowicz (4149_CR32) 2018; 30 RV Rao (4149_CR21) 2012; 44 Y Ho (4149_CR50) 2002; 115 S Mirjalili (4149_CR37) 2012; 218 J Ye (4149_CR35) 2007; 70 GX Liu (4149_CR40) 2017; 707 MJ Shafiee (4149_CR53) 2018; 48 YJ Zheng (4149_CR20) 2015; 55 MG Alejandro (4149_CR58) 2018; 117 YX Yang (4149_CR66) 2018; 10 L Wang (4149_CR74) 2018; 38 B Liu (4149_CR10) 2011; 186 XY Sui (4149_CR41) 2016; 85 A Kumar (4149_CR44) 2012; 83 GX Liu (4149_CR47) 2017; 129 HD Wang (4149_CR60) 2016; 20 4149_CR4 4149_CR48 S Hore (4149_CR39) 2017; 44 H Wang (4149_CR18) 2017; 382 H Wang (4149_CR19) 2016; 8 DE Goldberg (4149_CR11) 1988; 3 R Fukasawa (4149_CR69) 2016; 94 A Liu (4149_CR5) 2018; 38 G Antipov (4149_CR56) 2017; 72 XY Shi (4149_CR59) 2018; 30 X Yao (4149_CR34) 1997; 8 4149_CR3 KO Stanley (4149_CR54) 2019; 1 4149_CR2 |
| References_xml | – volume: 8 start-page: 33 issue: 1 year: 2016 end-page: 41 ident: CR19 article-title: Firefly algorithm with random attraction publication-title: Int J Bio Inspir Comput – volume: 107 start-page: 261 issue: C year: 2016 end-page: 270 ident: CR22 article-title: Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy publication-title: Knowl Based Syst – volume: 70 start-page: 875 issue: 4 year: 2007 end-page: 882 ident: CR35 article-title: A tabu based neural network learning algorithm publication-title: Neurocomputing – volume: 685 start-page: 310 year: 2017 end-page: 316 ident: CR43 article-title: Computational prediction of mechanical properties of a C-Mn weld metal based on the microstructures and micromechanical properties publication-title: Mater Sci Eng A – volume: 83 start-page: 169 issue: 2 year: 2012 end-page: 174 ident: CR44 article-title: Data-driven pareto optimization for microalloyed steels using genetic algorithms publication-title: Steel Res Int – volume: 55 start-page: 1 year: 2015 end-page: 11 ident: CR20 article-title: Water wave optimization: a new nature-inspired metaheuristic publication-title: Comput Oper Res – ident: CR4 – ident: CR51 – ident: CR12 – volume: 1 start-page: 24 issue: 1 year: 2019 end-page: 35 ident: CR54 article-title: Designing neural networks through neuroevolution publication-title: Nature Mach Intell doi: 10.1038/s42256-018-0006-z – volume: 20 start-page: 939 issue: 6 year: 2016 end-page: 952 ident: CR60 article-title: Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system publication-title: IEEE Trans Evolut Comput – volume: 28 start-page: 2911 issue: 12 year: 2017 end-page: 2923 ident: CR67 article-title: Airline passenger profiling based on fuzzy deep machine learning publication-title: IEEE Trans Neural Netw Learn Syst – ident: CR29 – volume: 72 start-page: 15 year: 2017 end-page: 26 ident: CR56 article-title: Effective training of convolutional neural networks for face-based gender and age prediction publication-title: Pattern Recognit – ident: CR25 – volume: 30 start-page: 871 issue: 3 year: 2018 end-page: 889 ident: CR55 article-title: Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity publication-title: Neural Comput Appl – volume: 117 start-page: 180 year: 2018 end-page: 191 ident: CR58 article-title: Evodeep: a new evolutionary approach for automatic deep neural networks parametrisation publication-title: J Parallel Distrib Comput – volume: 29 start-page: 1481 issue: 5 year: 2018 end-page: 1492 ident: CR38 article-title: A genetic approach to automatic neural network architecture optimization publication-title: Neural Comput Appl – volume: 94 start-page: 61 year: 2016 end-page: 79 ident: CR69 article-title: A disjunctive convex programming approach to the pollution-routing problem publication-title: Transp Res Part B Methodol – volume: 438 start-page: 95 year: 2018 end-page: 106 ident: CR6 article-title: A new dynamic firefly algorithm for demand estimation of water resources publication-title: Inf Sci – volume: 30 start-page: 3317 issue: 11 year: 2018 end-page: 3326 ident: CR59 article-title: Tracking topology structure adaptively with deep neural networks publication-title: Neural Comput Appl – volume: 8 start-page: 99 issue: 2 year: 2004 end-page: 110 ident: CR49 article-title: Meta-lamarckian learning in memetic algorithms publication-title: IEEE Trans Evolut Comput – volume: 22 start-page: 11 issue: 1 year: 2013 end-page: 20 ident: CR62 article-title: Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm publication-title: Neural Comput Appl – volume: 38 start-page: 2874 issue: 11 year: 2018 end-page: 2884 ident: CR5 article-title: A discrete fireworks algorithm for solving no-idle permutation flow shop problem publication-title: Syst Eng Theory Pract – volume: 30 start-page: 2531 issue: 8 year: 2018 end-page: 2538 ident: CR32 article-title: Evaluation of a pressure head and pressure zones in water distribution systems by artificial neural networks publication-title: Neural Comput Appl – volume: 11 start-page: 341 issue: 4 year: 1997 end-page: 359 ident: CR13 article-title: Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces publication-title: J Glob Optim – volume: 218 start-page: 11125 issue: 22 year: 2012 end-page: 11137 ident: CR37 article-title: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm publication-title: Appl Math Comput – volume: 24 start-page: 1221 issue: 5 year: 2014 end-page: 1228 ident: CR31 article-title: Discrimination of bio-crystallogram images using neural networks publication-title: Neural Comput Appl – ident: CR57 – volume: 35 start-page: 888 issue: 5 year: 2008 end-page: 894 ident: CR16 article-title: Chaotic annealing with hypothesis test for function optimization in noisy environments publication-title: Chaos Solitons Fractals – volume: 57 start-page: 1 year: 2017 end-page: 15 ident: CR7 article-title: An improved TLBO based memetic algorithm for aerodynamic shape optimization publication-title: Eng Appl Artif Intell – ident: CR36 – volume: 36 start-page: 779 issue: 3 year: 2016 end-page: 786 ident: CR71 article-title: Priority rule-based complex identical parallel machines scheduling publication-title: Syst Eng Theory Pract – volume: 3 start-page: 95 issue: 2 year: 1988 end-page: 99 ident: CR11 article-title: Genetic algorithms and machine learning publication-title: Mach Learn – volume: 30 start-page: 488 issue: 4 year: 2015 end-page: 510 ident: CR45 article-title: Evolutionary design of Nickel-based superalloys using data-driven genetic algorithms and related strategies publication-title: Mater Manuf Process – ident: CR26 – volume: 28 start-page: 15 issue: 1 year: 2017 end-page: 28 ident: CR33 article-title: Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models publication-title: Neural Comput Appl – volume: 48 start-page: 603 issue: 1 year: 2018 end-page: 613 ident: CR53 article-title: Deep learning with darwin: evolutionary synthesis of deep neural networks publication-title: Neural Process Lett – volume: 186 start-page: 231 issue: 1 year: 2011 end-page: 262 ident: CR10 article-title: A unified framework for population-based metaheuristics publication-title: Ann Oper Res – volume: 37 start-page: 18 issue: 1 year: 2007 end-page: 27 ident: CR1 article-title: An effective PSO-based memetic algorithm for flow shop scheduling publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) – ident: CR2 – volume: 85 start-page: 1395 issue: 5 year: 2016 end-page: 1403 ident: CR41 article-title: Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM publication-title: Int J Adv Manuf Technol – volume: 382 start-page: 374 year: 2017 end-page: 387 ident: CR18 article-title: Firefly algorithm with neighborhood attraction publication-title: Inf Sci – volume: 435 start-page: 124 year: 2018 end-page: 149 ident: CR65 article-title: A disease diagnosis and treatment recommendation system based on big data mining and cloud computing publication-title: Inf Sci – volume: 35 start-page: 1187 issue: 5 year: 2015 end-page: 1201 ident: CR70 article-title: Re-entrant flexible scheduling: models, algorithms and applications publication-title: Syst Eng Theory Pract – volume: 5 start-page: 1 issue: 4 year: 2018 end-page: 10 ident: CR8 article-title: Emergency drug procurement planning based on big-data driven morbidity prediction publication-title: IEEE Trans Ind Inform – volume: 52 start-page: 12673 issue: 36 year: 2013 end-page: 12688 ident: CR30 article-title: Neural networks applied in chemistry. II. Neuro-evolutionary techniques in process modeling and optimization publication-title: Ind Eng Chem Res – volume: 43 start-page: 634 issue: 2 year: 2013 end-page: 647 ident: CR14 article-title: Gaussian bare-bones differential evolution publication-title: IEEE Trans Cybern – year: 2018 ident: CR64 article-title: Parallel protein community detection in large-scale PPI networks based on multi-source learning publication-title: IEEE/ACM Trans Comput Biol Bioinform doi: 10.1109/TCBB.2018.2868088 – volume: 10 start-page: S867 issue: 7 year: 2018 end-page: S875 ident: CR66 article-title: Deep learning aided decision support for pulmonary nodules diagnosing: a review publication-title: J Thoracic Dis – volume: 115 start-page: 549 issue: 3 year: 2002 end-page: 570 ident: CR50 article-title: Simple explanation of the no-free-lunch theorem and its implications publication-title: J Optim Theory Appl doi: 10.1023/A:1021251113462 – ident: CR63 – ident: CR27 – volume: 22 start-page: 650 issue: 5 year: 2007 end-page: 658 ident: CR46 article-title: Genetic algorithms in optimization of strength and ductility of low-carbon steels publication-title: Mater Manuf Process – volume: 20 start-page: 4965 issue: 12 year: 2016 end-page: 4980 ident: CR9 article-title: Parameter estimation of nonlinear chaotic system by improved TLBO strategy publication-title: Soft Comput – ident: CR48 – ident: CR3 – volume: 8 start-page: 694 issue: 3 year: 1997 end-page: 713 ident: CR34 article-title: A new evolutionary system for evolving artificial neural networks publication-title: IEEE Trans Neural Netw – ident: CR52 – ident: CR17 – volume: 28 start-page: 919 issue: 4 year: 2017 end-page: 933 ident: CR61 article-title: A parallel random forest algorithm for big data in Spark cloud computing environment publication-title: IEEE Trans Parallel Distrib Syst – volume: 38 start-page: 54 year: 2018 end-page: 63 ident: CR74 article-title: A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem publication-title: Swarm Evolut Comput – year: 2018 ident: CR73 publication-title: Particle swarm optimization based memetic algorithms framework for scheduling of central planned and distributed flowshops, swarm intelligence: applications – volume: 2 start-page: 641 issue: 6 year: 1988 end-page: 648 ident: CR15 article-title: Teaching feed-forward neural networks by simulated annealing publication-title: Complex Syst – volume: 35 start-page: 619 year: 2012 end-page: 625 ident: CR42 article-title: Prediction of mechanical properties of Fe–Cr–Mo sintered steel in relationship with microstructure publication-title: Mater Des – volume: 44 start-page: 656 issue: 9 year: 2017 end-page: 665 ident: CR39 article-title: An adaptive neuro-fuzzy inference system-based modelling to predict mechanical properties of hot-rolled trip steel publication-title: Ironmak Steelmak – year: 2018 ident: CR68 article-title: Performance-aware model for sparse matrix-matrix multiplication on the sunway TaihuLight supercomputer publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2018.2871189 – volume: 44 start-page: 1447 issue: 12 year: 2012 end-page: 1462 ident: CR21 article-title: Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems publication-title: Eng Optim – volume: 70 start-page: 423 year: 2018 end-page: 464 ident: CR23 article-title: Soft computing based object detection and tracking approaches: state-of-the-art survey publication-title: Appl Soft Comput – ident: CR28 – ident: CR24 – volume: 129 start-page: 210 year: 2017 end-page: 218 ident: CR47 article-title: Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb–Si alloys publication-title: Mater Des – volume: 707 start-page: 452 year: 2017 end-page: 458 ident: CR40 article-title: Artificial neural network application to microstructure design of Nb–Si alloy to improve ultimate tensile strength publication-title: Mater Sci Eng A – volume: 46 start-page: 139 issue: 1 year: 2016 end-page: 149 ident: CR72 article-title: An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem publication-title: IEEE Trans Syst Man Cybern Syst – volume: 28 start-page: 15 issue: 1 year: 2017 ident: 4149_CR33 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2293-9 – ident: 4149_CR63 doi: 10.1016/j.ins.2018.06.045 – volume: 685 start-page: 310 year: 2017 ident: 4149_CR43 publication-title: Mater Sci Eng A doi: 10.1016/j.msea.2017.01.023 – volume: 5 start-page: 1 issue: 4 year: 2018 ident: 4149_CR8 publication-title: IEEE Trans Ind Inform – volume: 44 start-page: 1447 issue: 12 year: 2012 ident: 4149_CR21 publication-title: Eng Optim doi: 10.1080/0305215X.2011.652103 – ident: 4149_CR26 doi: 10.1109/ICCA.2017.8003085 – ident: 4149_CR28 doi: 10.1109/ICENCO.2015.7416344 – volume: 43 start-page: 634 issue: 2 year: 2013 ident: 4149_CR14 publication-title: IEEE Trans Cybern doi: 10.1109/TSMCB.2012.2213808 – volume: 55 start-page: 1 year: 2015 ident: 4149_CR20 publication-title: Comput Oper Res doi: 10.1016/j.cor.2014.10.008 – ident: 4149_CR29 doi: 10.1109/ICCES.2017.8275392 – volume: 35 start-page: 888 issue: 5 year: 2008 ident: 4149_CR16 publication-title: Chaos Solitons Fractals doi: 10.1016/j.chaos.2006.05.070 – ident: 4149_CR2 doi: 10.1109/CEC.2016.7743990 – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 4149_CR13 publication-title: J Glob Optim doi: 10.1023/A:1008202821328 – volume: 3 start-page: 95 issue: 2 year: 1988 ident: 4149_CR11 publication-title: Mach Learn doi: 10.1023/A:1022602019183 – ident: 4149_CR3 – volume-title: Particle swarm optimization based memetic algorithms framework for scheduling of central planned and distributed flowshops, swarm intelligence: applications year: 2018 ident: 4149_CR73 – volume: 20 start-page: 4965 issue: 12 year: 2016 ident: 4149_CR9 publication-title: Soft Comput doi: 10.1007/s00500-015-1786-2 – volume: 382 start-page: 374 year: 2017 ident: 4149_CR18 publication-title: Inf Sci doi: 10.1016/j.ins.2016.12.024 – volume: 35 start-page: 619 year: 2012 ident: 4149_CR42 publication-title: Mater Des doi: 10.1016/j.matdes.2011.10.011 – volume: 30 start-page: 3317 issue: 11 year: 2018 ident: 4149_CR59 publication-title: Neural Comput Appl doi: 10.1007/s00521-017-2906-y – volume: 57 start-page: 1 year: 2017 ident: 4149_CR7 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2016.10.009 – ident: 4149_CR48 doi: 10.1109/FSKD.2018.8686939 – volume: 1 start-page: 24 issue: 1 year: 2019 ident: 4149_CR54 publication-title: Nature Mach Intell doi: 10.1038/s42256-018-0006-z – volume: 35 start-page: 1187 issue: 5 year: 2015 ident: 4149_CR70 publication-title: Syst Eng Theory Pract – volume: 36 start-page: 779 issue: 3 year: 2016 ident: 4149_CR71 publication-title: Syst Eng Theory Pract – volume: 70 start-page: 875 issue: 4 year: 2007 ident: 4149_CR35 publication-title: Neurocomputing doi: 10.1016/j.neucom.2006.10.018 – volume: 22 start-page: 11 issue: 1 year: 2013 ident: 4149_CR62 publication-title: Neural Comput Appl doi: 10.1007/s00521-011-0741-0 – volume: 30 start-page: 2531 issue: 8 year: 2018 ident: 4149_CR32 publication-title: Neural Comput Appl doi: 10.1007/s00521-017-2844-8 – ident: 4149_CR25 doi: 10.1109/CEC.2016.7744029 – volume: 85 start-page: 1395 issue: 5 year: 2016 ident: 4149_CR41 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-015-8039-5 – volume: 30 start-page: 871 issue: 3 year: 2018 ident: 4149_CR55 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2714-9 – volume: 83 start-page: 169 issue: 2 year: 2012 ident: 4149_CR44 publication-title: Steel Res Int doi: 10.1002/srin.201100189 – volume: 38 start-page: 2874 issue: 11 year: 2018 ident: 4149_CR5 publication-title: Syst Eng Theory Pract – volume: 46 start-page: 139 issue: 1 year: 2016 ident: 4149_CR72 publication-title: IEEE Trans Syst Man Cybern Syst doi: 10.1109/TSMC.2015.2416127 – year: 2018 ident: 4149_CR64 publication-title: IEEE/ACM Trans Comput Biol Bioinform doi: 10.1109/TCBB.2018.2868088 – ident: 4149_CR57 doi: 10.1109/TPDS.2018.2877359 – volume: 38 start-page: 54 year: 2018 ident: 4149_CR74 publication-title: Swarm Evolut Comput doi: 10.1016/j.swevo.2017.06.001 – volume: 129 start-page: 210 year: 2017 ident: 4149_CR47 publication-title: Mater Des doi: 10.1016/j.matdes.2017.05.027 – volume: 72 start-page: 15 year: 2017 ident: 4149_CR56 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2017.06.031 – volume: 2 start-page: 641 issue: 6 year: 1988 ident: 4149_CR15 publication-title: Complex Syst – volume: 107 start-page: 261 issue: C year: 2016 ident: 4149_CR22 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2016.06.019 – ident: 4149_CR12 doi: 10.1109/ICNN.1995.488968 – volume: 24 start-page: 1221 issue: 5 year: 2014 ident: 4149_CR31 publication-title: Neural Comput Appl doi: 10.1007/s00521-013-1346-6 – volume: 186 start-page: 231 issue: 1 year: 2011 ident: 4149_CR10 publication-title: Ann Oper Res doi: 10.1007/s10479-011-0894-3 – volume: 29 start-page: 1481 issue: 5 year: 2018 ident: 4149_CR38 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2510-6 – volume: 30 start-page: 488 issue: 4 year: 2015 ident: 4149_CR45 publication-title: Mater Manuf Process doi: 10.1080/10426914.2014.984203 – volume: 438 start-page: 95 year: 2018 ident: 4149_CR6 publication-title: Inf Sci doi: 10.1016/j.ins.2018.01.041 – volume: 52 start-page: 12673 issue: 36 year: 2013 ident: 4149_CR30 publication-title: Ind Eng Chem Res doi: 10.1021/ie4000954 – volume: 435 start-page: 124 year: 2018 ident: 4149_CR65 publication-title: Inf Sci doi: 10.1016/j.ins.2018.01.001 – volume: 37 start-page: 18 issue: 1 year: 2007 ident: 4149_CR1 publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) doi: 10.1109/TSMCB.2006.883272 – volume: 28 start-page: 919 issue: 4 year: 2017 ident: 4149_CR61 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2016.2603511 – volume: 70 start-page: 423 year: 2018 ident: 4149_CR23 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2018.05.023 – year: 2018 ident: 4149_CR68 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2018.2871189 – volume: 707 start-page: 452 year: 2017 ident: 4149_CR40 publication-title: Mater Sci Eng A doi: 10.1016/j.msea.2017.09.039 – volume: 44 start-page: 656 issue: 9 year: 2017 ident: 4149_CR39 publication-title: Ironmak Steelmak doi: 10.1080/03019233.2016.1227025 – volume: 20 start-page: 939 issue: 6 year: 2016 ident: 4149_CR60 publication-title: IEEE Trans Evolut Comput doi: 10.1109/TEVC.2016.2555315 – volume: 48 start-page: 603 issue: 1 year: 2018 ident: 4149_CR53 publication-title: Neural Process Lett doi: 10.1007/s11063-017-9733-0 – volume: 22 start-page: 650 issue: 5 year: 2007 ident: 4149_CR46 publication-title: Mater Manuf Process doi: 10.1080/10426910701323607 – volume: 8 start-page: 99 issue: 2 year: 2004 ident: 4149_CR49 publication-title: IEEE Trans Evolut Comput doi: 10.1109/TEVC.2003.819944 – volume: 28 start-page: 2911 issue: 12 year: 2017 ident: 4149_CR67 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2016.2609437 – volume: 117 start-page: 180 year: 2018 ident: 4149_CR58 publication-title: J Parallel Distrib Comput doi: 10.1016/j.jpdc.2017.09.006 – ident: 4149_CR4 doi: 10.1109/CEC.2016.7744111 – volume: 94 start-page: 61 year: 2016 ident: 4149_CR69 publication-title: Transp Res Part B Methodol doi: 10.1016/j.trb.2016.09.006 – ident: 4149_CR27 doi: 10.1109/ICCSE.2018.8468778 – ident: 4149_CR17 doi: 10.1007/978-3-642-13495-1_44 – volume: 8 start-page: 694 issue: 3 year: 1997 ident: 4149_CR34 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.572107 – ident: 4149_CR52 doi: 10.1007/978-3-540-37258-5_149 – ident: 4149_CR36 doi: 10.1007/978-3-540-72395-0_28 – volume: 218 start-page: 11125 issue: 22 year: 2012 ident: 4149_CR37 publication-title: Appl Math Comput – ident: 4149_CR24 – volume: 115 start-page: 549 issue: 3 year: 2002 ident: 4149_CR50 publication-title: J Optim Theory Appl doi: 10.1023/A:1021251113462 – volume: 10 start-page: S867 issue: 7 year: 2018 ident: 4149_CR66 publication-title: J Thoracic Dis doi: 10.21037/jtd.2018.02.57 – ident: 4149_CR51 doi: 10.1109/MC.2013.6608208 – volume: 8 start-page: 33 issue: 1 year: 2016 ident: 4149_CR19 publication-title: Int J Bio Inspir Comput doi: 10.1504/IJBIC.2016.074630 |
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| SubjectTerms | 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 |
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| Title | Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization |
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