Multi-objective optimization design of induction magnetometer based on improved chemical reaction algorithm

References

• Lenz J, Edelstein AS. Magnetic sensors and their applications. IEEE Sens J. 2006;6(3):631–649.10.1109/JSEN.2006.874493
   Web of Science ®Google Scholar
• Ripka P. Induction sensors magnetic sensors and magnetometers. Boston (MA): Artech House; 2001. p. 47–74.
   Google Scholar
• Constable SC, Orange AS, Hoversten GM, et al. Marine magnetotellurics for petroleum exploration part I: a sea-floor equipment system. Geophysics. 2012;63(3):816–825.
   Web of Science ®Google Scholar
• Yan B, Zhu W, Liu L, et al. An optimization method for induction magnetometer of 0.1 mHz to 1 kHz. IEEE Trans Magn. 2013;49(10):5294–5300.10.1109/TMAG.2013.2264821
   Web of Science ®Google Scholar
• Broad Band Induction Coil Magnetometer MTC-80 [Online]. Available from: www.phoenix-geophysics.com
   Google Scholar
• Lukoschus DG. Optimization theory for induction-coil magnetometers at higher frequencies. IEEE Trans Geosci Elect. 1979;17(3):56–63.10.1109/TGE.1979.294613
   Google Scholar
• HC Séran, Fergeau P. An optimized low-frequency three-axis search coil magnetometer for space research. Rev Sci Instrum. 2005;76(4):044502-044502-10.
   Web of Science ®Google Scholar
• Grosz A, Paperno E. Analytical optimization of low-frequency search coil magnetometers. IEEE Sens J. 2012;12(12):2719–2723.10.1109/JSEN.2012.2202179
   Web of Science ®Google Scholar
• Duan H, Luo Q. Adaptive backtracking search algorithm for induction magnetometer optimization. IEEE Trans Magn. 2014;50(12):1–6.10.1109/TMAG.2014.2342192
   Web of Science ®Google Scholar
• Liu K, Zhu W, Yan B, et al. Ultralow noise preamplifier and optimization method for induction magnetometers. IEEE Sens J. 2015;15(6):3293–3300.10.1109/JSEN.2015.2389336
   Web of Science ®Google Scholar
• Zhou D, Li Y, Jiang B, et al. A novel multiobjective quantum-behaved particle swarm optimization based on the ring model. Math Probl Eng. 2016:1–15.
   Google Scholar
• Lin Q, Chen J, Zhan ZH, et al. A hybrid evolutionary immune algorithm for multiobjective optimization problems. IEEE Trans Evol Comput. 2016;20(5):711–729.
   Web of Science ®Google Scholar
• Jiang S, Feng L, Chen KH, et al. Adaptive indicator-based evolutionary algorithm for multiobjective optimization problems. IEEE Congr Evol Comput. 2016:492–499.
   Google Scholar
• Moussouni F, Brisset S, Brochet P. Some results on the design of brushless DC wheel motor using SQP and GA. Int J Appl Electromagnet Mech. 2007;26:233–241.
   Web of Science ®Google Scholar
• dos Santos Coelho L, Barbosa LZ, Lebensztajn L. Multiobjective particle swarm approach for the design of a brushless DC wheel motor. IEEE Trans Magn. 2010;46(8):2994–2997.10.1109/TMAG.2010.2044145
   Web of Science ®Google Scholar
• Zhou D, Li X, Pan Q, et al. Multiobjective weapon-target assignment problem by two-stage evolutionary multiobjective particle swarm optimization. IEEE International Conference on Information and Automation (ICIA), 2016, Ningbo, Zhejiang, China. IEEE; 2016. p. 921–926.10.1109/ICInfA.2016.7831950
   Google Scholar
• Chen X, Luo Z, He X, et al. Thinning and weighting of planar arrays by modified teaching–learning-based optimization algorithm. J Electromagn Waves Appl. 2014;28(15):1924–1934.10.1080/09205071.2014.950432
   Web of Science ®Google Scholar
• Kurgan P, Kozieł S. Surrogate-assisted multi-objective optimization of compact microwave couplers. J Electromagn Waves Appl. 2016;30(15):2067–2075.10.1080/09205071.2016.1230523
   Web of Science ®Google Scholar
• Lam YS, Xu J, Li VOK. Chemical reaction optimization for population transition in peer-to-peer live streaming. Proceedings of the IEEE Congress on Evolutionary Computation; July; Barcelona, Spain; 2010, p. 1–8.
   Google Scholar
• Xu J, Lam AYS, Li VOK. Chemical reaction optimization for task scheduling in grid computing. IEEE Trans Parallel Distrib Syst. 2011;22(10):1624–1631.
   Web of Science ®Google Scholar
• Yu JJQ, Lam AYS, Li VOK, Evolutionary artificial neural network based on chemical reaction optimization. Proceedings of the IEEE Congress on Evolutionary Computation, New Orleans, LA, June. 2011, p. 2083–2090.
   Google Scholar
• Pan B, Lam AYS, LiVOK. Network coding optimization based on chemical reaction optimization. Proceedings of the IEEE Global Communications Conference; December; Houston, TX; 2011. p. 1–5.F.
   Google Scholar
• Bhattacharjee K, Bhattacharya A, Dey SHN. Oppositional real coded chemical reaction optimization for different economic dispatch problems. Int J Electr Power Energy Syst. 2014;55(2):378–391.10.1016/j.ijepes.2013.09.033
   Web of Science ®Google Scholar
• Yu JJQ, Lam AYS, Li VOK. Adaptive chemical reaction optimization for global numerical optimization. Evol Comput IEEE. 2015;55:3192–3199.
   Google Scholar
• Duan H, Li S, Shi Y. Predator–prey brain storm optimization for DC brushless motor. IEEE Trans Magn. 2013;49(10):5336–5340.10.1109/TMAG.2013.2262296
   Web of Science ®Google Scholar
• Brest J, Zumer V, Maucec MS. Self-adaptive differential evolution algorithm in constrained real-parameter optimization. Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 2006. p. 215–222.
   Google Scholar
• Fu Y, Ding M, Zhou C. Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans Syst Man Cybern Part A Syst Humans. 2012;42(2):511–526.10.1109/TSMCA.2011.2159586
   Web of Science ®Google Scholar
• Osborn JA. Demagnetizing factors of the general ellipsoid. Phys Rev. 1945;67(11–12):351–357.10.1103/PhysRev.67.351
   Web of Science ®Google Scholar
• Tumanski S. Induction coil sensors – a review. Meas Sci Technol. 2007;18(3):R31–R46.10.1088/0957-0233/18/3/R01
   Web of Science ®Google Scholar
• Lam AYS, Li VOK, Yu JJQ. Real-coded chemical reaction optimization. IEEE Trans Evol Comput. 2012;16(3):339–353.10.1109/TEVC.2011.2161091
   Web of Science ®Google Scholar