Abstract
As stated in Sect. 10.3, the problem of identifying or reconstructing a given quantity, based on known data e.g. measurements, is called an inverse problem. Loosely speaking, an inverse problem is one in which an effect is measured and the cause of it is to be determined.
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References
Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61(5):2745–2757
Costa Silva MA, Coelho LS, Lebensztajn L (2012) Multiobjective biogeography-based optimization based on predator-prey approach. IEEE Trans Magnetics 48(2):951–954
Di Barba P (2010) Multiobjective shape design in electricity and magnetism. Springer
Di Barba P (2016) Multi-objective wind-driven optimisation and magnet design. Electron Lett 52(14):1216–1218
Di Barba P, Dughiero F, Forzan M, Mognaschi ME, Sieni E (2018) New solutions to a multi-objective benchmark problem of induction heating: an application of computational biogeography and evolutionary algorithms. Arch Electr Eng 67(1):139–149
Di Barba P, Dughiero F, Mognaschi ME, Savini A, Wiak S (2016) Biogeography-inspired multiobjective optimization and MEMS design. IEEE Trans Magn 52(3)
Di Barba P, Gotszalk T, Majstrzyk W, Mognaschi ME, Orłowska K, Wiak S, Sierakowski A (2018) Optimal design of electromagnetically actuated MEMS cantilevers. Sensors (Switzerland) 18(8)
Di Barba P, Mognaschi ME (2009) Industrial design with multiple criteria: shape optimization of a permanent-magnet generator. IEEE Trans Magn 45(3):1482–1485
Di Barba P, Mognaschi ME, Przybylski M, Rezaei N, Slusarek B, Wiak S (2018) Geometry optimization for a class of switched-reluctance motors: a bi-objective approach. Int J Appl Electromagnet Mech 56(S1):S107–S122
Di Barba P, Mognaschi ME, Rezaei N, Lowther DA, Rahman T (2019) Many-objective Shape Optimisation of IPM Motors for Electric Vehicle Traction. in press on Int J Appl Electromagnetics Mech IJAEM
Di Barba P, Mognaschi ME, Savini A (2007) Synthesizing a field source for magnetic stimulation of peripheral nerves. IEEE Trans Magn 43(11):4023–4029
Di Barba P, Mognaschi ME, Savini A, Wiak S (2016) Island biogeography as a paradigm for MEMS optimal design. Int J Appl Electromagnetics Mech IJAEM 51(s1):97–105
Di Barba P, Mognaschi ME, Venini P, Wiak S (2017) Biogeography-inspired multiobjective optimization for helping MEMS synthesis. Arch Electr Eng 66(3):607–623
Di Barba P, Mognaschi ME, Wiak S, Przybylski M, Slusarek B (2018) Optimization and measurements of switched reluctance motors exploiting soft magnetic composite. Int J Appl Electromagnet Mech 57(S1):S83–S93
Di Barba P, Savini A, Wiak S (2008) Field models in electricity and magnetism. Springer, Berlin, Germany
Di Barba P, Savini A, Wiak S (2017) Higher-order multiobjective design of MEMS. Int J Appl Electromagnet Mech 53(S2):S239–S247
Di Barba P, Wiak S (2015) Evolutionary computing and optimal design of MEMS. IEEE/ASME Trans Mechatron 20(4):1660–1667
Macarthur RH, Wilson EO (1967) The theory of island biogeography. Princeton University Press
Mognaschi ME (2017) Micro biogeography-inspired multi-objective optimisation for industrial electromagnetic design. Electron Lett 53(22)
Roy PK, Ghoshal SP, Thakur S (2010) Multi-objective optimal power flow using biogeography based optimization. Electric Power Compon Syst 38:1406–1426
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Singh U, Kumar H, Kamal TS (2010) Design of Yagi-Uda antenna using biogeography based optimization. IEEE Trans Antennas Propag 58(10):3375–3379
Singh S, Mittal E, Sachdeva G (2012) NSBBO for gain-impedance optimization of Yagi-Uda antenna design. Proc World Congress Inf Commun Technol 2012:856–860
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Di Barba, P., Mognaschi, M.E. (2020). Numerical Methods for MEMS Design: Automated Optimization. In: MEMS: Field Models and Optimal Design. Lecture Notes in Electrical Engineering, vol 573 . Springer, Cham. https://doi.org/10.1007/978-3-030-21496-8_11
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