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混合磁悬浮装置的各项参数相互影响,决定着整个装置的性能。在满足承重要求的条件下,有必要对该装置的各项参数进行优化研究。为此,提出一种多策略改进粒子群算法,并将其应用到混合磁悬浮承重装置的参数优化中。首先,对混合磁悬浮装置进行介绍,通过分析永磁和电磁悬浮力,以励磁损耗和资金投入最小,和在允许范围内减载程度最高为目标,建立该装置的优化模型。在算法上,通过分析传统粒子群算法的缺陷,首次提出多开端策略来提高种群的多样性,结合反向学习和参数修正等多种策略对粒子群算法进行改进(多策略改进粒子群算法),以广义Schwefel函数为验证函数,通过与其他粒子群算法的比较证明,改进算法具有更强的优势。最后,运用多策略改进粒子群算法对磁悬浮模型进行优化,将优化结果与原有参数进行比较,分析可知该结果更加符合实际情况,通过仿真验证该结果的合理性,为进一步建立实验模型奠定了理论基础。
Mixed magnetic levitation device parameters affect each other, determines the performance of the entire device. Under the condition of meeting the load-bearing requirements, it is necessary to optimize the various parameters of the device. Therefore, a multi-strategy improved particle swarm optimization algorithm is proposed and applied to the parameter optimization of hybrid maglev bearing device. Firstly, the hybrid magnetic levitation device is introduced. By analyzing the permanent magnet and electromagnetic levitation force, the optimization model of the device is established with the goal of minimizing excitation loss and capital investment and maximizing the degree of de-loading within the allowable range. In the algorithm, by analyzing the flaws of the traditional particle swarm optimization algorithm, a multi-start strategy is proposed for the first time to improve the diversity of the swarm. Combined with multiple learning strategies such as reverse learning and parameter modification, the PSO is improved (multi-strategy improved particle swarm optimization) The generalized Schwefel function is used as a verification function. Compared with other particle swarm optimization algorithms, the improved algorithm has more advantages. Finally, the multi-strategy improved particle swarm optimization algorithm is used to optimize the magnetic suspension model. The optimization results are compared with the original parameters. The analysis shows that the result is more in line with the actual situation. The rationality of the result is verified by simulation, which lays the foundation for further establishment of the experimental model Theoretical basis.