Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing
static path planning problems. Nevertheless, such application on dynamic
scenarios has been severely precluded by PSO's low computational efficiency and
premature convergence downsides. To address these limitations, we proposed a
Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor
operations, thereby enhancing computational efficiency. Harnessing the
computational advantage of TOF, a variant of PSO, designated as Self-Evolving
Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by
a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous
optimization of its own hyper-parameters to evade premature convergence.
Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation
(AT) mechanism that substantially elevates the real-time performance of SEPSO
on dynamic path planning problems were introduced. Comprehensive experiments on
four widely used benchmark optimization functions have been initially conducted
to corroborate the validity of SEPSO. Following this, a dynamic simulation
environment that encompasses moving start/target points and dynamic/static
obstacles was employed to assess the effectiveness of SEPSO on the dynamic path
planning problem. Simulation results exhibit that the proposed SEPSO is capable
of generating superior paths with considerably better real-time performance (67
path planning computations per second in a regular desktop computer) in
contrast to alternative methods. The code of this paper can be accessed here.

利用张量操作形式的自进化粒子群优化 (SEPSO) 可提高计算效率并在动态路径规划中生成出色路径，具有优越的实时性能。