Study on Multi-objective Collaborative Optimization of Switched Reluctance Motor Based on Chaos Fruit Fly Optimization Algorithm

doi:10.16182/j.issn1004731x.joss.201807026

Abstract

Abstract: Switched reluctance motor (SRM) is difficult to get the best solution due to its complicated process and high professional requirement. Therefore, a collaborative multi-objective optimization method based on chaotic fruit fly algorithm is proposed in the paper. The initial solution is obtained by traditional design method, and the performance is verified by finite element analysis (FEA). The extreme learning machine (ELM) is applied to obtain the non-parameter model of SRM based on the sample data from FEA. The chaotic fruit fly algorithm is proposed for design optimization. Simulation results demonstrate that better coefficient of torque ripple and efficiency can be obtained, and it has the advantages of fewer parameters, faster convergence and is not easy to fall into local optimal solution. Moreover, better application value can be achieved.

Key words: SRM, multi-objective collaborative optimization, chaos fruit fly optimization algorithm, extreme learning machine

CLC Number:

TM352

Zhang Xiaoping, Rao Shenghua, Zhang zhu, Zhao xuan. Study on Multi-objective Collaborative Optimization of Switched Reluctance Motor Based on Chaos Fruit Fly Optimization Algorithm[J]. Journal of System Simulation, 2018, 30(7): 2640-2647.

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