• Journal of Inorganic Materials
  • Vol. 34, Issue 1, 27 (2019)
Shi-Yu DU1, Yi-Ming ZHANG1, Kan LUO1、2, Qing HUANG1, [in Chinese]1, [in Chinese]1, [in Chinese]1、2, and [in Chinese]1
Author Affiliations
  • 11. Engineering Laboratory of Nuclear Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
  • 22. School of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
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    DOI: 10.15541/jim20180214 Cite this Article
    Shi-Yu DU, Yi-Ming ZHANG, Kan LUO, Qing HUANG, [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Design of the Nature-inspired Algorithms Library and Its Significance for New Materials Research and Development[J]. Journal of Inorganic Materials, 2019, 34(1): 27 Copy Citation Text show less
    References

    [1] D XIANG X, , X SUN. A combinatorial approach to materials discovery. Science, 268, 1738(1995).

    [2] J ZHU, X XIE J, Y HUANG H. Recent progress and new ideas for accelerating research in rare earth steel. Journal of Iron and Steel Research, 29, 513-529(2017).

    [3] W LU, S RAMAKRISHNA, T ZHANG et al. Materials informatics. Journal of Intelligent Manufacturing, 2018, 1-20.

    [4] Y LI, D SHI, Z LIU et al. The development of cladding materials for the accident tolerant fuel system from the Materials Genome Initiative. Scripta Materialia, 143, 129-136(2018).

    [5] . The development of material genome technology in the field of new energy materials. Energy Storage Science and Technology, 6, 990(2017).

    [6] A WHITE A. Big data are shaping the future of materials science. Mrs Bulletin, 38, 594-595(2013).

    [7] A CHOUDHARY, L WARD, A AGRAWAL et al. A general- purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2, 16028(2016).

    [8] N MOUNET, M GIBERTINI, P SCHWALLER et al. Two- dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nature Nanotechnology, 13, 246-252(2018).

    [9] X LI, S XU, Y ZHAO et al. Two-dimensional semiconducting boron monolayers. Journal of the American Chemical Society, 139, 17233-17236(2017).

    [10] M JIN H, B SULLIVAN M, L TAN T et al. High-throughput survey of ordering configurations in MXene alloys across compositions and temperatures. ACS Nano, 11, 4407-4418(2017).

    [11] J ZHOU, J LIN, X HUANG et al. A library of atomically thin metal chalcogenides. Nature, 556, 355(2018).

    [12] R KINCAID D, L LAWSON C, J HANSON R et al. Basic linear algebra subprograms for Fortran usage. ACM Transactions on Mathematical Software (TOMS), 5, 308-323(1979).

    [13] C BISCHOF, Z BAI, E ANDERSON et al. LAPACK Users' Guide. Society for Industrial and Applied Mathematics, Philadelphia, PA. Society for Industrial and Applied Mathematics(1999).

    [14] C SANDERSON, R CURTIN. Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software(2016).

    [15] . Intel® Math Kernel Library Developer Reference(2017).

    [16] T HEATH M, W DEMMEL J, . Parallel numerical linear algebra. Acta Numerica, 2, 111-197(1993).

    [17] L KETTNER. N A HER S, GOODMAN J E, et al. Two Computational Geometry Libraries: LEDA and CGAL. Handbook of Discrete and Computational Geometry, Chapman & Hall/. CRC, 1435-1463(2004).

    [18] A BAKSHEEV, K PULLI, K KORNYAKOV et al. Real time computer vision with OpenCV. Queue, 10, 40(2012).

    [19] N CHAKRABORTI. Genetic algorithms in materials design and processing. International Materials Reviews, 49, 246-260(2004).

    [20] W PASZKOWICZ. Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Materials and Manufacturing Processes, 24, 174-197(2009).

    [21] B HKDH. Neural networks in materials science. ISIJ international, 39, 966-979(1999).

    [22] H BHADESHIA. Neural networks and information in materials science. Statistical Analysis and Data Mining: The ASA Data Science Journal, 1, 296-305(2009).

    [23] S FORSIK, C DIMITRIU R, H BHADESHIA et al. Performance of neural networks in materials science. Materials Science and. Technology., 25, 504-510(2009).

    [24] S YANG, J EVANS, M ZHANG Y. Revisiting Hume-Rothery's rules with artificial neural networks. Acta Materialia, 56, 1094-1105(2008).

    [25] J EVANS, F YANG S, M ZHANG Y. Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations. Philosophical Magazine, 90, 4453-4474(2010).

    [26] R EVANS J, Y ZHANG, S YANG. Corrected values for boiling points and enthalpies of vaporization of elements in handbooks. Journal of Chemical and Engineering Data, 56, 328-337(2011).

    [27] M ZHANG Y, F XUE D, R UBIC et al. Predicting the structural stability and formability of ABO3-type perovskite compounds using artificial neural networks. Materials Focus, 1, 57-64(2012).

    [28] J GUAY, E CLOUTIER, R NADEAU. New evidence about the existence of a bandwagon effect in the opinion formation process. International Political Science Review, 14, 203-213(1993).

    [29] J EARMAN, J MOSTERIN. A critical look at inflationary cosmology. Philosophy of Science, 66, 1-49(1999).

    [30] V TRIMBLE. Existence and nature of dark matter in the universe. Annual Review of Astronomy & Astrophysics, 25, 425-472(1987).

    [31] N ROSEN, A EINSTEIN, B PODOLSKY. Can quantum- mechanical description of physical reality be considered complete?. Phys. Rev., 47, 777-780(1935).

    [32] E SHANNON C. A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423(1948).

    [33] X YANG. A New Metaheuristic Bat-inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010),. Springer, 65-74(2010).

    [34] A SAHAI, K KHAN. A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. International Journal of Intelligent Systems and Applications, 4, 23(2012).

    [35] X YANG, M NIGDELI S, G BEKDA C S. A novel bat algorithm based optimum tuning of mass dampers for improving the seismic safety of structures. Engineering Structures, 159, 89-98(2018).

    [36] B VAINSTEIN, A KHACHATURYAN, S SEMENOVSKAYA. Statistical-thermodynamic approach to determination of structure amplitude phases. Sov. Phys. Crystallography, 24, 519-524(1979).

    [37] S SEMENOVSOVSKAYA, B VAINSHTEIN, A KHACHATURYAN. The thermodynamic approach to the structure analysis of crystals. Acta Crystallographica Section A: Crystal Physics, Diffraction,. Theoretical and General Crystallography, 37, 742-754(1981).

    [38] P VECCHI M, D GELATT C, S KIRKPATRICK. Optimization by simulated annealing. Science, 220, 671-680(1983).

    [39] L ANDERSON H. METROPOLIS. Monte Carlo and the Maniac. Los alamos Science, 14, 96-108(1986).

    [40] . Monte-Carlo Algorithms in Graph Isomorphism Testing. Université tde Montréal Technical Report, DMS(1979).

    [41] A LEVIN L. The tale of one-way functions. Problems of Information Transmission, 39, 92-103(2003).

    [42] D GRUNDY. Concepts and Calculation in Cryptography. Citeseer(2008).

    [43] R QUINLAN J. Induction of decision trees. Machine Learning, 1, 81-106(1986).

    [44] R QUINLAN J. C4.5: Programs for Machine Learning. Elsevier(2014).

    [45] M COULOM R E. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. Springer, 72-83(2006).

    [46] L KOCSIS. SZEPESV A RI C. Bandit Based Monte-Carlo Planning. Springer, 282-293(2006).

    [47] D SILVER, J MADDISON C, A HUANG et al. Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484-489(2016).

    [48] D SILVER, K SIMONYAN, J SCHRITTWIESER et al. Mastering the game of go without human knowledge. Nature, 550, 354(2017).

    [49] H LIU Y, W ZHANG, L FAN. Ecological Pyramid Particle Swarm Optimization. Computer Science, 44, 237-244(2017).

    [50] V RAO R, P VAKHARIA D, J SAVSANI V. Teaching- learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303-315(2011).

    [51] P VAKHARIA D, V RAO R, J SAVSANI V. Teaching- learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183, 1-15(2012).

    [52] S TUO, F DENG, L YONG. Survey of teaching-learning-based optimization algorithms. Application Research of Computers, 30, 1933-1938(2013).

    [53] X BI, J WANG. Teaching-learning-based optimization algorithm with hybrid learning strategy. Journal of Zhejiang University (Engineering Science), 51, 1024-1031(2017).

    [54] Y TAN, K LIU, J ZHANG et al. Random Black Hole Particle Swarm Optimization and Its Application. IEEE, 359-365(2008).

    [55] A HATAMLOU. Black hole: a new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184(2013).

    [56] D WARNANA D. OTHERS. Black hole algorithm for determining model parameter in self-potential data. Journal of Applied Geophysics, 148, 189-200(2018).

    [57] Y LIU, Y ZHU, L MA et al. A novel bionic algorithm inspired by plant root foraging behaviors. Applied Soft Computing, 37, 95-113(2015).

    [58] S DAN. Biogeography-based optimization. IEEE Transactions on. Evolutionary Computation., 12, 702-713(2008).

    [59] W HUBERT, T WESCHE, C GOERTLER. Modified habitat suitability index model for brown trout in Southeastern Wyoming. North American Journal of Fisheries Management, 7, 232-237(1987).

    [60] C WANG, X DUAN, N WANG et al. Survey of Biogeography- based Optimization. Computer Science, 37, 34-38(2010).

    [61] H MA, D SIMON, P SIARRY et al. Biogeography-based optimization: a 10-year review. IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 391-407(2017).

    [62] P BENIOFF. The computer as a physical system: a microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines. Journal of Statistical Physics, 22, 563-591(1980).

    [63] P FEYNMAN R. Simulating physics with computers. International Journal of Theoretical Physics, 21, 467-488(1982).

    [64] D DEUTSCH. Quantum theory, the Church-Turing principle and the universal quantum computer. Proc. R. Soc. Lond. A, 400, 97-117(1985).

    [65] S GILDERT, W JOHNSON M, H AMIN M et al. Quantum annealing with manufactured spins. Nature, 473, 194(2011).

    [66] S KNYSH, D VENTURELLI, S MANDRA et al. Quantum optimization of fully connected spin glasses. Physical Review X, 5, 31040(2015).

    [67] M HOSKINSON E, W JOHNSON M, I BUNYK P et al. Architectural considerations in the design of a superconducting quantum annealing processor. IEEE Transactions on Applied Superconductivity, 24, 1-10(2014).

    [68] H WANG, Y HE, H LI Y et al. High-efficiency multiphoton boson sampling. Nature Photonics, 11, 361-365(2017).

    [69] H CANTU S, V VENKATRAMANI A, Y LIANG Q et al. Observation of three-photon bound states in a quantum nonlinear medium. Science, 359, 783(2018).

    [70] R GOOGLE. A Preview of Bristlecone, Google's New Quantum Processor..

    [71] S DEVABHAKTUNI, N CHARI A, D BECKMAN et al. Efficient networks for quantum factoring. Physical Review A, 54, 1034-1063(1996).

    [72] K GROVER L. A Fast Quantum Mechanical Algorithm for Database Search. STOC’96 Proceedings of the twenty-annaal ACM Symposium on Theory of. Computing, 212-219(1996).

    [73] K GROVER L. From Schrödinger's equation to the quantum search algorithm. Pramana, 56, 333-348(2001).

    [74] K GROVER L. Quantum computing. Sciences, 39, 24-30(1999).

    [75] . AKL M N S G, 558-559(2000).

    [76] R SIMON D. On the Power of Quantum Computation. Society for Industrial and Applied Mathematics, 1759-1768(1997).

    [77] N PASCAL KOIRAN V, N PORTIER. A quantum lower bound for the query complexity of Simon's problem. Lecture Notes in Computer Science, 3580, 1287-1298(2005).

    [78] R JOZSA. Quantum factoring, discrete logarithms, and the hidden subgroup problem. Computing in Science & Engineering, 3, 34-43(2000).

    [79] W SHOR P. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a. Quantum Computer., 303-332(1999).

    [80] P BUHLER J. JR H W L, POMERANCE C. Factoring integers with the number field sieve. OAI, 5, 231-253(1993).

    [81] , K LENSTRA A. The Development of the. Number Field Sieve. Springer-Verlag, 564-572(1993).

    [82] 2: 15023-1-17. MONTANARO A(2016).

    [83] K GROVER L. Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett., 79, 325-328(1997).

    [84] G BRASSARD, , M BOYER. Tight Bounds on Quantum Searching. Wiley‐VCH Verlag GmbH & Co. KGaA, 493-505(1998).

    [85] A AMBAINIS, M CHILDS A, W REICHARDT B et al. Any AND-OR Formula of Size N can be Evaluated in time N1/2 + o(1) on a Quantum Computer., 363-372(2007).

    [86] S ZHANG, X SUN, C YAO A. Graph Properties and Circular Functions: How Low Can Quantum Query Complexity Go?, 286-293(2004).

    [87] G BRASSARD. HØYER P, MOSCA M, et al. Quantum amplitude amplification and estimation. Quantum Computation &. Information., 5494, 53-74(2002).

    [88] U SCHÖNING. A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems, 410(1999).

    [89] W HARROW A, A HASSIDIM, S LLOYD. Quantum algorithm for linear systems of equations. Physical Review Letters, 103, 150502(2009).

    [90] S GUTMANN, J GOLDSTONE, E FARHI et al. Quantum Computation by Adiabatic Evolution. Quantum Physics, arxiv: quant-ph/0001106..

    [91] X SUN. A survey on quantum computing. Scientia Sinica Informationis, 46, 982(2016).

    [92] P. WITTEK(2014).

    [93] A NARAYANAN, M MOORE. Quantum-inspired. Genetic Algorithms., 61-66(1996).

    [94] H KIM J, H HAN K. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 6, 580-593(2002).

    [95] Z ZHUANG, J YANG, L SHI. Multi-universe parallel quantum genetic algorithm. Acta Electronica Sinica, 32, 923-928(2004).

    [96] H CHEN, C ZHANG, J ZHANG. Chaos Updating Rotated Gates Quantum-inspired. Genetic Algorithm., 1108-1112(2004).

    [97] F TANG, H WU, L WANG. Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation. Applied Mathematics and Computation, 171, 1141-1156(2005).

    [98] L WANG. Advances in quantum-inspired evolutionary algorithms. Control and Decision, 23, 1321-1326(2008).

    [99] P PYLLKKÄNEN, P PYLLKKÖ. New Directions in Cognitive Science. Creating Consilience: Integrating the Sciences & the. Humanities.(1995).

    [100] S KAK. On. Quantum Neural Computing. Elsevier Science Inc., 143-160(1995).

    [101] C KAK S. The Three Languages Of The Brain: Quantum, Reorganizational, and. Associative., 185-219(1996).

    [102] A GAUTAM, S KAK. Symbols, meaning, and origins of mind. Biosemiotics, 6, 301-310(2013).

    [103] B LUDERMIR T, R DE OLIVEIRA W, . Quantum perceptron over a field and neural network architecture selection in a quantum computer. Neural Networks, 76, 55-64(2016).

    [104] G MARTINELLI, M PANELLA. Neural networks with quantum architecture and quantum learning. International Journal of Circuit Theory & Applications, 39, 61-77(2011).

    [105] M SCHULD, I SINAYSKIY, F PETRUCCIONE. The quest for a Quantum Neural Network. Quantum Information Processing, 13, 2567-2586(2014).

    [106] A TIWARI, O PATEL, V PATEL et al. Quantum Based Neural Network Classifier and Its Application for Firewall to. Detect Malicious Web Request., 67-74(2015).

    [107] J LI. Quantum-inspired neural networks with application. Open Journal of Applied Sciences, 5, 233-239(2015).

    [108] E KAPUTKINA N, V ALTAISKY M, A KRYLOV V. Quantum neural networks: current status and prospects for development. Physics of Particles &. Nuclei., 45, 1013-1032(2014).

    [109] J SUN, W FANG, Z XIE et al. Convergence analysis of quantum- behaved particle swarm optimization algorithm and study on its control parameter. Acta Physica Sinica, 6, 3686-3694(2009).

    [110] J NIGAM M, A MANJU. Applications of quantum inspired computational intelligence: a survey. Artificial Intelligence Review, 42, 79-156(2014).

    [111] T HOOFT G. The cellular automaton interpretation of quantum mechanics. Physics Today, 70, 60(2017).

    [112] S LLOYD. A theory of quantum gravity based on quantum computation. Quantum Physics(2018).

    [113] M YING. Recent progress in the research of quantum programming. Communcations of the CCF, 13, 21-27(2017).

    [114] X YANG, S PATNAIK, K NAKAMATSU. Nature-Inspired Computing and Optimization: Theory and Applications. Springer(2017).

    [115] X YANG. Nature-inspired Computation in Engineering. Springer(2016).

    [116] R CHIONG. Nature-inspired Algorithms for Optimisation. Springer(2009).

    [117] K DU, M SWAMY. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhäuser(2016).

    [118] X. YANG(2010).

    [119] J NEWALL, E BURKE, G KENDALL et al. Hyper-heuristics: An Emerging Direction in Modern Search Technology. Handbook of Metaheuristics,. Springer, 457-474(2003).

    [120] A DELORME. Genetic Algorithm for Optimization of Mechanical Properties. Technical report,. University of Cambridge(2003).

    [121] J PEI, M KAMBER, J HAN. Data Mining: Concepts and Techniques. Elsevier(2011).

    [122] I RAICU, Y ZHAO, I FOSTER et al. Cloud Computing and Grid Computing 360-degree Compared. IEEE, 1-10(2008).

    [123] R BOUTABA, L CHENG, Q ZHANG. Cloud computing: state- of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7-18(2010).

    [124] X YANG, I FISTER, I FISTER JR et al. A brief review of nature- inspired algorithms for optimization. Elektrotehniški Vestnik, 80, 116-122(2013).

    [125] X YANG. Recent Advances in Swarm Intelligence and Evolutionary Computation. Springer(2015).

    [126] X YANG, M KARAMANOGLU. Swarm Intelligence and Bio-inspired Computation: An Overview. Swarm Intelligence and Bio-Inspired Computation,. Elsevier, 3-23(2013).

    [127] H REISSNER. Über die eigengravitation des elektrischen Feldes nach der Einsteinschen theorie. Annalen der Physik, 355, 106-120(1916).

    [128] K SCHWARZSCHILD. Über das Gravitationsfeld einer Kugel aus inkompressibler Flüssigkeit nach der Einsteinschen theorie.(1916).

    [129] J DROSTE. On the field of a single centre in Einstein's theory of gravitation. Koninklijke Nederlandse Akademie van Wetenschappen Proceedings Series B Physical Sciences, 17, 998-1011(1915).

    [130] W HAWKING S. Black hole explosions?. Nature, 248, 30-31(1974).

    [131] S. DATTA(2015).

    [132] J APOSTOLAKIS. An introduction to data mining. Structure & Bonding, 134, 1-35(2009).

    [133] M STEINBACH, T PANGNING, V KUMAR. Introduction to data mining. Data Analysis in the Cloud, 22, 1-25(2014).

    [134] S DATTA, P CHATTOPADHYAY P. Soft computing techniques in advancement of structural metals. International Materials Reviews, 58, 475-504(2013).

    [135] S DATTA, K BANERJEE M. Fuzzy modeling of strength- composition-process parameter relationships of HSLA steels. Materials and Manufacturing Processes, 20, 761-776(2005).

    [136] S DATTA, Q ZHANG, M MAHFOUF et al. Imprecise knowledge based design and development of titanium alloys for prosthetic applications. Journal of the Mechanical Behavior of Biomedical Materials, 53, 350-365(2016).

    [137] S DATTA, P DEY, S DEY et al. Rough set approach to predict the strength and ductility of TRIP steel. Materials and Manufacturing Processes, 24, 150-154(2009).

    [138] J SINGH, S GILL S. Fuzzy modeling and simulation of ultrasonic drilling of porcelain ceramic with hollow stainless steel tools. Materials and Manufacturing Processes, 24, 468-475(2009).

    [139] S DATTA, P CHATTOPADHYAY P, S DEY et al. Modeling the properties of TRIP steel using AFIS: a distributed approach. Computational Materials Science, 43, 501-511(2008).

    [140] V BALACHANDRAN P, R DEHGHANNASIRI, D XUE et al. Optimal experimental design for materials discovery. Computational Materials Science, 129, 311-322(2017).

    [141] M GONG, E LUO, H LI et al. A multiobjective cooperative coevolutionary algorithm for hyperspectral sparse unmixing. IEEE Transactions on Evolutionary Computation, 21, 234-248(2017).

    [142] Z WANG, M GONG, Z ZHU et al. A similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication system. IEEE Transactions on Evolutionary Computation, 21, 878-897(2017).

    [143] S YANG X. Nature-Inspired Optimization Algorithms. Elsevier Science Publishers B. V., 1292(2014).

    [144] E FRANK, A HALL M, H WITTEN I et al. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann(2016).

    Shi-Yu DU, Yi-Ming ZHANG, Kan LUO, Qing HUANG, [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Design of the Nature-inspired Algorithms Library and Its Significance for New Materials Research and Development[J]. Journal of Inorganic Materials, 2019, 34(1): 27
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