Xiong Hui, Zhu Jibin, Liu Jinzhen
School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China.
Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Aug 25;42(4):716-723. doi: 10.7507/1001-5515.202412058.
The therapeutic effects of transcranial magnetic stimulation (TMS) are closely related to the structure of the stimulation coil. Based on this, this study designed an A-word coil and proposed a multi-strategy fusion multi-objective slime mould algorithm (MSSMA) aimed at optimizing the stimulation depth, focality, and intensity of the coil. MSSMA significantly improved the convergence and distribution of the algorithm by integrating a dual-elite guiding mechanism, a hyperbolic tangent control strategy, and a hybrid polynomial mutation strategy. Furthermore, compared with other stimulation coils, the novel coil optimized by the MSSMA demonstrates superior performance in terms of stimulation depth. To verify the optimization effects, a magnetic field measurement system was established, and a comparison of the measurement data with simulation data confirmed that the proposed algorithm could effectively optimize coil performance. In summary, this study provides a new approach for deep TMS, and the proposed algorithm holds significant reference value for multi-objective engineering optimization problems.
经颅磁刺激(TMS)的治疗效果与刺激线圈的结构密切相关。基于此,本研究设计了一种A字线圈,并提出了一种多策略融合多目标黏菌算法(MSSMA),旨在优化线圈的刺激深度、聚焦性和强度。MSSMA通过集成双精英引导机制、双曲正切控制策略和混合多项式变异策略,显著提高了算法的收敛性和分布性。此外,与其他刺激线圈相比,经MSSMA优化的新型线圈在刺激深度方面表现出卓越的性能。为验证优化效果,建立了磁场测量系统,测量数据与模拟数据的比较证实了所提算法能够有效优化线圈性能。综上所述,本研究为深部TMS提供了一种新方法,所提算法对多目标工程优化问题具有重要的参考价值。