School of Electrical Engineering and Automation, TIANGONG University, Tianjin 300387, China; Key Laboratory of Advanced Electrical Engineering and Energy Technology, TIANGONG University, Tianjin 300387, China.
School of Electrical Engineering and Automation, TIANGONG University, Tianjin 300387, China; Key Laboratory of Advanced Electrical Engineering and Energy Technology, TIANGONG University, Tianjin 300387, China.
Artif Intell Med. 2020 Apr;104:101790. doi: 10.1016/j.artmed.2020.101790. Epub 2020 Jan 3.
Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of double layer coil array. To balance the exploration and exploitation abilities, three novel improved strategies are used in NMSPSO based on multi-swarm particle swarm optimizer. Firstly, a novel information exchange strategy is achieved by individual exchanges between sub-swarms. Secondly, a novel leaning strategy is used to control knowledge dissemination in the population, which not only increases the diversity of the particles but also guarantees the convergence. Finally, a novel mutation strategy is introduced, which can help the population jump out of the local optimum for better exploration ability. The method is examined on a set of well-known benchmark functions and the results show that NMSPSO has better performance than many particle swarm optimization variants. And the superior electric field distribution in mTMS can be obtained by NMSPSO to optimize the current configuration of the double layer coil array.
多通道经颅磁刺激（mTMS）是一种改善精神疾病的治疗方法，具有灵活的工作模式，可用于不同的应用。为了使大脑中的电场分布满足治疗预期，我们开发了一种新型的多群粒子群优化算法（NMSPSO）来优化双层线圈阵列的电流配置。为了平衡探索和开发能力，基于多群粒子群优化算法，我们在 NMSPSO 中使用了三种新的改进策略。首先，通过子群之间的个体交换实现了一种新颖的信息交换策略。其次，采用了一种新的学习策略来控制种群中的知识传播，这不仅增加了粒子的多样性，而且保证了收敛性。最后，引入了一种新的变异策略，可以帮助种群跳出局部最优，以获得更好的探索能力。该方法在一组著名的基准函数上进行了检验，结果表明，NMSPSO 比许多粒子群优化变体具有更好的性能。并且可以通过 NMSPSO 优化双层线圈阵列的电流配置来获得更好的 mTMS 电场分布。
