基于多种群遗传算法的永磁涡流驱动器的多目标优化设计

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Abstract The aim of this paper was to explore the use of the multiple population genetic algorithm (MPGA) to optimize several parameters of permanent magnet eddy current drivers .At first,on the basis of the magnetic field analysis model,the analytical formulas of key parameters were deduced.By using permanent magnet thickness,pole-arc coefficient and copper plate thickness as variables and taking output torque,rotational inertia and the volume of the driver as optimization goals,this paper proposed a multi-objective optimization function with entropy coefficients and used the multiple population genetic algorithm to optimize parameters structure of the driver.Then,3D finite element analysis (3D-FEA) and experimental results proved the validity and feasibility of the proposed method.The results confirm that compared with other two optimization algorithms,optimization design result by the multiple population genetic algorithm based on the analytical model has better effect on optimization of structural parameters.

Key words： Permanent magnet eddy current driver analytical method entropy-based weight multi-objective optimization multiple population genetic algorithm

Received: 20 August 2016 Published: 23 March 2017

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TM144

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	Shi Tongyu
	Wang Dazhi
	Li Zhao

Cite this article:

Shi Tongyu,Wang Dazhi,Li Zhao. Multi-Objective Optimization Design of PMECD by Multiple Population Genetic Algorithm[J]. Transactions of China Electrotechnical Society, 2016, 31(增刊2): 262-268.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I增刊2/262

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