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.

翻译：粒子群优化（PSO）在解决静态路径规划问题中已展现出有效性。然而，由于PSO存在的计算效率低下与早熟收敛缺陷，该方法在动态场景中的应用受到严重限制。针对这些局限，本文提出张量运算形式（TOF），将个体粒子操作转换为张量运算，从而提升计算效率。借助TOF的计算优势，我们开发了一种PSO变体——自进化粒子群优化（SEPSO）。SEPSO基于新型层次化自进化框架（HSEF），可自主优化自身超参数以避免早熟收敛。此外，本文引入先验初始化（PI）机制与自动截断（AT）机制，显著提升了SEPSO在动态路径规划问题中的实时性能。通过四项广泛使用的基准优化函数进行了系统性实验，初步验证了SEPSO的有效性。随后，采用包含运动起点/目标点及动态/静态障碍物的动态仿真环境，评估了SEPSO在动态路径规划问题中的效果。仿真结果表明，相比其他方法，所提出的SEPSO能够生成更优路径，并具备显著更佳的实时性能（在常规台式计算机上每秒完成67次路径规划计算）。本文代码可在此处获取。