An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation

Highlights：

• Three improvement strategies are used in the NMSPSO algorithm to balance the exploration and exploitation abilities. These three strategies and their role are as follows: (a) Information exchange strategy: Guarantee the rational flow of information in the population. (b) Learning strategy: Tradeoff between population diversity and convergence rate. (c) Mutation strategy: Enable the population to quickly jump out of the local optimal solution.

• NMSPSO algorithm is examined on a set of well-known benchmark functions and the results show that the NMSPSO has better performance than many particle swarm optimization variants.

• By optimizing the current configuration of the coil array in mTMS with NMSPSO algorithm, the electric field can be focused at the target and the electric field focusing degree can be improved compared to several other PSO variant algorithms.

摘要

•In order to optimize the electric field distribution in the brain to meet the treatment expectations of multichannel transcranial magnetic stimulation (mTMS), this paper presents a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of coil array in mTMS.•Three improvement strategies are used in the NMSPSO algorithm to balance the exploration and exploitation abilities. These three strategies and their role are as follows: (a) Information exchange strategy: Guarantee the rational flow of information in the population. (b) Learning strategy: Tradeoff between population diversity and convergence rate. (c) Mutation strategy: Enable the population to quickly jump out of the local optimal solution.•NMSPSO algorithm is examined on a set of well-known benchmark functions and the results show that the NMSPSO has better performance than many particle swarm optimization variants.•By optimizing the current configuration of the coil array in mTMS with NMSPSO algorithm, the electric field can be focused at the target and the electric field focusing degree can be improved compared to several other PSO variant algorithms.