论文标题
在具有障碍的空间中使用粒子群优化作为探路策略
Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles
论文作者
论文摘要
粒子群优化（PSO）是一种基于随机和基于种群的自适应优化的搜索算法。在本文中，提出了一种探索策略，以提高广泛应用程序的路径计划效率。这项研究旨在研究PSO参数（粒子数量，重量常数，粒子常数和全局常数）对算法性能的影响，以提供溶液路径。增加PSO参数会使群体移动更快到目标点，但由于过多的随机运动，需要很长时间才能收敛，反之亦然。从具有不同参数的多种模拟中，PSO算法被证明能够在具有障碍物的空间中提供解决方案路径。
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.
