摘要
针对数控切削参数优化问题的非线性和多约束性质,采用一种元胞粒子群算法(CPSO)进行优化。在基本粒子群算法(PSO)思想的基础上,引入邻居的概念,以搜索解空间的局部信息,并将粒子的信息交流范围扩展到种群外部,从而能搜索到更有希望的解空间;在罚函数机制的基础上,引入标志变量记录粒子是否曾经满足过所有约束条件,根据标志变量进行粒子个体极值与种群全局极值的更新。通过比较CPSO算法与其他算法取得的结果,验证该算法解决数控切削参数优化问题的有效性和优越性。
Aiming at the nonlinearity and multi-constraints of NC cutting parameter optimization problem, an optimiza- tion method based on Cellular Particle Swarm Optimization（CPSO） algorithm is employed. In basis of Particle Swarm Optimization （PSO） algorithm, the concept of neighbor is introduced in order to search the local information in solution space and to extend the scope of information communication among particles out of the population, and thus to search more hopeful solution space. Based on penalty function mechanism, mark variable is introduced to record whether a parti- cle has ever satisfied all of the constraints. The individual extreme value of a particle as well as the global extreme value of the population updates according to mark variable. By comparing the optimization result acquired by CPSO algorithm with results gained from other algorithms, its validity and superiority on solving NC cutting parameter optimization prob- lem can be confirmed.
出处
《计算机工程与应用》
CSCD
2014年第2期252-257,共6页
Computer Engineering and Applications
基金
国家自然科学基金重点项目(No.51035001)
国家自然科学基金项目(No.50875101)
国家重点基础研究发展规划(973)(No.2011CB706804)
关键词
数控加工
切削参数
元胞粒子群算法
约束处理
Numerical Control（NC） machining
cutting parameter
cellular particle swarm optimization algorithm
constraints processing
