Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles

TL;DR

This paper explores how particle swarm optimization can be used as an effective pathfinding strategy in obstacle-rich environments, analyzing parameter effects on efficiency and convergence.

Contribution

It introduces a PSO-based pathfinding method and investigates how different parameters influence its performance in obstacle-laden spaces.

Findings

PSO can successfully find paths in obstacle environments

Parameter tuning affects convergence speed and solution quality

Higher parameters increase speed but may slow convergence

Abstract

Particle swarm optimization (PSO) is a search algorithm based on stochastic and population-based adaptive optimization. In this paper, a pathfinding strategy is proposed to improve the efficiency of path planning for a broad range of applications. This study aims to investigate the effect of PSO parameters (numbers of particle, weight constant, particle constant, and global constant) on algorithm performance to give solution paths. Increasing the PSO parameters makes the swarm move faster to the target point but takes a long time to converge because of too many random movements, and vice versa. From a variety of simulations with different parameters, the PSO algorithm is proven to be able to provide a solution path in a space with obstacles.