计算机工程与应用Issue(16)：252-258,7.DOI:10.3778/j.issn.1002-8331.1308-0409
应用于铣削参数优化的粒子群和遗传交互算法
马超 1蔡军 1杨飞 1崔彬1
作者信息
- 1. 国电南瑞科技股份有限公司，南京 210061
- 折叠
摘要
Abstract
There are many non-linear, multi-extremum and multidimensional complicated problems in the applications of the engineering field. This paper puts forward the Genetic Algorithm(GA)into the Particle Swarm Algorithm(PSO), and uses the method of mutual learning to solve those problems. This method integrates the particle swarm algorithm’s simple theory and quick convergence with the genetic algorithm’s global search ability to get higher convergence precision, stronger execution and avoid falling into local optimal solution. The comparative analysis results show the parallel learning strategy has great advantages in terms of accuracy, efficiency and processing ability of different complexity problems. This algorithm is especially applicable to solve accurately and complex problems. Example shows that this algorithm can solve the nonlinear, multiple maximum and multi-dimension engineering problem existed in the milling parameters opti-mization solved by the machine dynamics theory.关键词
粒子群算法/遗传算法/交互学习/机械动力学/铣削参数优化Key words
Particle Swarm Optimization/Genetic Algorithm/mutual learning/machine dynamics/milling parameters opti-mization分类
信息技术与安全科学引用本文复制引用
马超,蔡军,杨飞,崔彬..应用于铣削参数优化的粒子群和遗传交互算法[J].计算机工程与应用,2015,(16):252-258,7.
