面向钻削路径规划问题的微粒群优化算法研究  

Particle Swarm Optimization Algorithm for Drilling Path Planning Problem

作　　者：朱光宇[1] 

机构地区：[1]福州大学机械工程及自动化学院,福建福州350002

出　　处：《信息与控制》2008年第1期103-107,112,共6页Information and Control

基　　金：福州大学科技发展基金资助项目(2006-XQ-15);福建省青年人才基金资助项目(2006F3074)

摘　　要：提出一种基于微粒群优化(PSO)算法的方法,用于解决离散空间的群孔钻削路径规划问题.为了满足钻削路径规划问题中整数编码的需要,建立了算子中元素的二元转换方法和操作方式,对算法的操作算子进行改进.针对基本微粒群算法全局收敛率较低的问题,本文在算法数学模型的基础上,引入了重新生成"停止进化微粒"的方式对算法加以改进.实验表明,改进的算法全局收敛率较基本算法提高3倍多;新的算法具有实现简单、收敛速度快、能够实现全局收敛的优点.实际应用中,采用新的PSO优化算法对钻削路径优化后,可以节省17.9%的机床工作台移动时间.Based on particle swarm optimization （PSO） algorithm, an approach is presented to solve the drilling path planning problem in discrete space. In order to meet the needs of integer coding in drilling path planning, a duality conversion method and an operating mode for the operator elements are established to improve the operator of the algorithm. As for the problem of low global convergence rate in standard PSO, a method based on mathematical model is introduced to regenerate the stop evolution particles and to improve the algorithm. Experiment indicates that the global convergence rate of the improved PSO is increased more than 3 times over that of the standard PSO, and that the improved algorithm has the characteristics of easy realization, fast convergence speed and better global convergence capability. In practical applications, the new PSO is used to optimize the drilling path, and the time spent on moving the worktable is saved by 17.9%.

关 键 词：微粒群优化(PSO)算法 路径规划 钻削 

分 类 号：TH162.1[机械工程—机械制造及自动化]