机械制造与自动化2023，Vol.52Issue(6)：81-84,4.DOI:10.19344/j.cnki.issn1671-5276.2023.06.020
基于改进粒子群算法的路径规划研究与应用
Research and Application of Path Planning Based on Improved Particle Swarm Optimization
摘要
Abstract
In order to improve the low convergence accuracy and premature in the basic particle swarm algorithm applicable to the traveling salesman problem,an improved adaptive hybrid annealing particle swarm(IAHAPSO)algorithm was proposed.The algorithm adopts the population dispersion-based adaptive adjustment of the inertia weight to guide the correct evolutionary direction of the population,applies the simulated annealing algorithm to update the population extreme value strategy so as to avoid the particle search falling into the local optimal solution,and introduces the genetic hybridization operator to increase the diversity of the population in the process of population development.Its feasibility and superiority in solution accuracy and efficiency are verified by three standard TSPLIB test sets,and its effectiveness is further verified by a four-axis cutting machine test system.关键词
旅行商问题/粒子群优化/模拟退火/遗传算法/路径规划Key words
travel salesman problem/particle swarm optimization/simulated annealing/genetic algorithm/path planning分类
信息技术与安全科学引用本文复制引用
董林威,高宏力,潘江..基于改进粒子群算法的路径规划研究与应用[J].机械制造与自动化,2023,52(6):81-84,4.基金项目
国家自然科学基金项目(51775452) （51775452）
