Comparison Between Genetic and Particle Swarm Optimization Algorithms in Optimizing Ships’ Degaussing Coil Currents
Keywords:
Degaussing system, ferromagnetic material, genetic algorithm, magnetic anomaly, magnetization, particle swarm optimizationAbstract
This paper presents a comparison between two well-known evolutionary algorithms in optimization of the degaussing coils currents of a ship which are used to reduce the magnetic anomalies of the ferromagnetic hull of the ships induced by the Earth’s magnetic field. To achieve this, first the magnetic anomalies of a simple model of a ship and the effect of each degaussing coil are simulated by using 3D finite element analysis (FEA) software. Then, both genetic algorithm and particle swarm optimization are used to find the best fitting coil currents which can reduce the anomalies of the ship. Using these algorithms is much simpler than optimizing this problem in FEA software in which a huge amount of numerical analyses are needed. This comparison will show which of these algorithms works better in this specific problem.
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References
K. J. Lee, G. Jeung, C.-S. Yang, H.-J. Chung, J. G. Park, H.-G. Kim, et al., “Implementation of material sensitivity analysis for determining unknown remanent magnetization of a ferromagnetic thin shell,” IEEE Trans. Magnetics, vol. 45, pp. 1478- 1481, 2009.
N.-S. Choi, G. Jeung, C.-S. Yang, H.-J. Chung, and D.-H. Kim, “Optimization of degaussing coil currents for magnetic silencing of a ship taking the ferromagnetic hull effect into account,” IEEE Trans. Applied Superconductivity, vol. 22, 2012.
N.-S. Choi, G. Jeung, S. S. Jung, C.-S. Yang, H.-J. Chung, and D.-H. Kim, “Efficient methodology for optimizing degaussing coil currents in ships utilizing magnetomotive force sensitivity information,” IEEE Trans. Magnetics, vol. 48, pp. 419-422, 2012.
H. Liu and Z. Ma, “Optimization of vessel degaussing system based on poly-population particle swarm algorithm,” ICMA. Int. Mechatronics and Automation Conf., Harbin, HL, pp. 3133-3137, August 2007.
J. J. Holmes, Exploitation of a Ship's Magnetic Field Signatures, Synthesis Lectures on Computational Electromagnetics, Morgan & Claypool Publishers, San Rafael, Calif., 2006.
J. J. Holmes, Modeling a Ship’s Ferromagnetic Signatures, Synthesis Lectures on Computational Electromagnetics, Morgan & Claypool Publishers, San Rafael, Calif., 2007.
J. J. Holmes, Reduction of a Ship's Magnetic Field Signatures, Synthesis Lectures on Computational Electromagnetics, Morgan & Claypool Publishers, San Rafael, Calif., 2008.
E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based optimization algorithms,” Advanced Engineering Informatics, vol. 19, pp. 43-53, 2005.
M. Clerc, Particle Swarm Optimization, John Wiley & Sons, 2010.
A. Lazinica, Particle Swarm Optimization, InTech, Kirchengasse, 2009.
S. Sivanandam and S. Deepa, Genetic Algorithm Optimization Problems, Springer, Berlin Heidelberg, 2008.
R. L. Haupt and D. H. Werner, Genetic Algorithms in Electromagnetics, John Wiley & Sons, 2007.
