Otwarty dostęp

Ability of Black-Box Optimisation to Efficiently Perform Simulation Studies in Power Engineering


Zacytuj

Kimiaei M, Neumaier A. Efficient Global Unconstrained Black Box Optimization. Mathematical Programming Optimization. 2022;14: 365-414. https://doi.org/10.1007/s12532-021-00215-9 Search in Google Scholar

Custódio AL, Scheinberg K, Vicente LN. Methodologies and Software for Derivative-free Optimization. In Advances and Trends in Optimization with Engineering Applications (SIAM). 2017: 495-506. https:///doi.org/10.1137/1.9781611974683.ch37 Search in Google Scholar

Rios LM, Sahinidis NV. Derivative-free optimization: a review of algorithms and comparison of software implementations. Journal of Global Optimization. 2013;56: 1247-1293. https://doi.org/10.1007/s10898-012-9951-y Search in Google Scholar

Larson J, Menickelly M, Wild SM. Derivative-free Optimization Methods. Acta Numerica. 2019;28: 287-404. https://doi.org/10.1017/S0962492919000060 Search in Google Scholar

Amaran S et al. Simulation Optimization: A Review of Algorithms and Applications. Ann Oper Res. 2016;240: 351–380. https://doi.org/10.1007/s10479-015-2019-x Search in Google Scholar

Ammeri A, Hachicha W, Chabchoub H, Masmoudi F. A comprehensive literature review of mono-objective simulation optimization methods. Advances in Production Engineering & Management. 2011;6(4): 291–302. Search in Google Scholar

Walton S, Hassan O, Morgan K. Selected Engineering Applications of Gradient Free Optimisation Using Cuckoo Search and Proper Orthogonal Decomposition. Archives of Computational Methods in Engineering. 2013;20: 123-154. https://doi.org/10.1007/s11831-013-9083-7 Search in Google Scholar

Yang XS, Deb S. Engineering Optimisation by Cuckoo Search. International Journal of Mathematical Modelling and Numerical Optimisation. 2010;1(4): 330–343. https://doi.org/10.48550/arXiv.1005.2908 Search in Google Scholar

Xing XQ, Damodaran M. Assessment of Simultaneous Perturbation Stochastic Approximation Method for Wing Design Optimization. Journal of Aircraft. 2002;39: 379–381. https://doi.org/10.2514/2.2939 Search in Google Scholar

Xing XQ, Damodaran M. Application of Simultaneous Perturbation Stochastic Approximation Method for Aerodynamic Shape Design Optimization. AIAA Journal. 2005;43(2): 284–294. https://doi.org/10.2514/1.9484 Search in Google Scholar

Xing XQ, Damodaran M. Inverse Design of Transonic Airfoils Using Parallel Simultaneous Perturbation Stochastic Approximation. Journal of Aircraft. 2005;42(2): 568–570. http://dx.doi.org/10.2514/1.10876 Search in Google Scholar

Kothandaraman G, Rotea MA. Simultaneous-Perturbation-Stochastic-Approximation Algorithm for Parachute Parameter Estimation. Journal of Aircraft. 2005;42(5): 1229–1235. http://dx.doi.org/10.2514/1.11721 Search in Google Scholar

Prakash P et al. Design Optimization of a Robust Sleeve Antenna for Hepatic Microwave Ablation. Physics in Medicine and Biology. 2008;53: 1057–1069. https://doi.org/10.1088/0031-9155/53/4/016 Search in Google Scholar

Li Y. A Simulation-based Evolutionary Approach to LNA Circuit Design Optimization. Applied Mathematics and Computation. 2009; 209(1): 57–67. http://dx.doi.org/10.1016/j.amc.2008.06.015 Search in Google Scholar

Radac MB et al. Application of IFT and SPSA to Servo System Control. IEEE Transactions on Neural Networks. 2011;22(12): 2363–2375. https://doi.org/10.1109/tnn.2011.2173804 Search in Google Scholar

Ernst D et al. The Cross-Entropy Method for Power System Combinatorial Optimization Problems. Power Tech. IEEE. 2007: 1290–1295. https://doi.org/10.1109/PCT.2007.4538502 Search in Google Scholar

Kowalczyk Ł, Elsner W, Niegodajew P. The Application of Non-Gradient Optimization Methods to New Concept of Power Plant. 6th IC-EpsMsO; 2015 Jul 8-11; Athens. Search in Google Scholar

Lu S. Dynamic modelling and simulation of power plant systems. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. 1999;213(1): 7-22. https://doi.org/10.1243/0957650991537392 Search in Google Scholar

Huan J et al. The Application of Digital Twin on Power Industry. IOP Conf. Series: Earth and Environmental Science. 2021;647. https://doi.org/10.1088/1755-1315/647/1/012015 Search in Google Scholar

Huyer W, Neumaier A. Global Optimization by Multilevel Coordinate Search. Journal of Global Optimization. 1999;14(2): 331-355. https://doi.org/10.1023/A:1008382309369 Search in Google Scholar

Huyer W, Neumaier A. SNOBFIT - Stable noisy optimization by branch and fit. ACM Transactions on Mathematical Software. 2008; 35(2): Article No.: 9, 1-25. https://doi.org/10.1145/1377612.1377613 Search in Google Scholar

Knysh P, Korkolis Y. blackbox: A procedure for parallel optimization of expensive black-box functions. arXiv (cs.MS). preprint submitted 2016, https://doi.org/10.48550/arXiv.1605.00998 Search in Google Scholar

Knysh P. blackbox: A Python module for parallel optimization of expensive black-box functions [Internet]. [place unknown]; [publisher unknown]; 2016 Feb 19 [updated 2022 Sep 5; cited 2021 Oct 17]. Available from: https://github.com/paulknysh/blackbox Search in Google Scholar

Roberts M. Extreme Learning. The Unreasonable Effectiveness of Quasirandom Sequences [Internet]. [place unknown]; [publisher unknown]; 2018 Apr 25 [cited 2022 June 2]. Available from: http://extremelearning.com.au/unreasonable-effectiveness-ofquasirandom-sequences/ Search in Google Scholar

Regis RG, Shoemaker CA. Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions. Journal of Global Optimization. 2005;31: 153-171. https://doi.org/10.1007/s10898-004-0570-0 Search in Google Scholar

Najko32. MCS algorithm [Internet]. [place unknown]; [publischer unknown]; 2018 August 6 [cited 2023 February 9]. Available from: https://commons.wikimedia.org/wiki/File:MCS_algorithm.gif Search in Google Scholar

Nelder JA, Mead R. A Simplex Method for Function Minimization. The Computer Journal. 1965;7: 308-313. https://doi.org/10.1093/comjnl/7.4.308 Search in Google Scholar

Winfield D. Function Minimization by Interpolating in a Data Table. IMA Journal of Applied Mathematics. 1973;12: 339-347. https://doi.org/10.1093/imamat/12.3.339 Search in Google Scholar

Holland JH. Genetic Algorithms and Adaptation. Adaptive Control for III-Defined Systems. 1984: 317-333. https://doi.org/10.1007/978-1-4684-8941-5_21 Search in Google Scholar

Sacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and Analysis of Computer Experiments. Statistical Science. 1989;4: 409-423. https://doi.org/10.1214/ss/1177012413 Search in Google Scholar

Jung I et al. Computational Fluid Dynamics Based Optimal Design of Guiding Channel Geometry in U-Type Coolant Layer Manifold of Large-Scale Microchannel Fischer–Tropsch Reactor. Ind. Eng. Chem. Res. 2016;55: 505–515. https://doi.org/10.1021/acs.iecr.5b03313 Search in Google Scholar

Hemmat Esfe M, Hajmohammad M, Moradi R, Abbasian Arani AA. Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by NSGA-II using response surface method. Applied Thermal Engineering. 2017;112: 1648–1657. https://doi.org/10.1016/j.applthermaleng.2016.10.129 Search in Google Scholar

Abdollahi A, Shams M. Optimization of heat transfer enhancement of nanofluid in a channel with winglet vortex generator. Applied Thermal Engineering. 2015;91: 1116–1126. https://doi.org/10.1016/j.applthermaleng.2015.08.066 Search in Google Scholar

Arora A, Bajaj I, Iyer SS, Hasan MMF. Optimal synthesis of periodic sorption enhanced reaction processes with application to hydrogen production. Computers & Chemical Engineering. (2018);115: 89–111. https://doi.org/10.1016/j.compchemeng.2018.04.004 Search in Google Scholar

Iyer SS, Bajaj I, Balasubramanian P, Hasan MMF. Integrated Carbon Capture and Conversion To Produce Syngas: Novel Process Design, Intensification, and Optimization. Industrial & Engineering Chemistry Research. (2017);56(30): 8622–8648. Search in Google Scholar

https://doi.org/10.1021/acs.iecr.7b01688 Search in Google Scholar

Liu J, Ploskas N, Sahinidis NV. Tuning BARON using derivative-free optimization algorithms. Journal of Global Optimization. 2019;74(4): 611–637. https://doi.org/10.1007/s10898-018-0640-3 Search in Google Scholar