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Particle Swarm Optimization (PSO) has demonstrated efficacy in addressingstatic path planning problems. Nevertheless, such application on dynamicscenarios has been severely precluded by PSO's low computational efficiency andpremature convergence downsides. To address these limitations, we proposed aTensor Operation Form (TOF) that converts particle-wise manipulations to tensoroperations, thereby enhancing computational efficiency. Harnessing thecomputational advantage of TOF, a variant of PSO, designated as Self-EvolvingParticle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned bya novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomousoptimization 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 SEPSOon dynamic path planning problems were introduced. Comprehensive experiments onfour widely used benchmark optimization functions have been initially conductedto corroborate the validity of SEPSO. Following this, a dynamic simulationenvironment that encompasses moving start/target points and dynamic/staticobstacles was employed to assess the effectiveness of SEPSO on the dynamic pathplanning problem. Simulation results exhibit that the proposed SEPSO is capableof generating superior paths with considerably better real-time performance (67path planning computations per second in a regular desktop computer) incontrast to alternative methods. The code and video of this paper can beaccessed here.

Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic Scenarios