摘要
为了提高平面度误差评定的计算精度和收敛速度,提出了一种混合Jaya算法的平面度误差评定算法。首先,基于最小区域原则,构建平面度误差的数学模型;其次,在基本Jaya算法的基础上,两次引入反向学习机制对解的质量进行改善;再次,在迭代过程中,引入下山单纯形法提高算法的局部搜索能力;最后,通过两组算例对所提的算法进行实验验证。结果表明,混合Jaya算法在计算精度上优于遗传算法,粒子群算法,模拟植物生长算法,基本Jaya算法以及人工蜂群算法,在迭代速度上,快于上述算法的同时,相较于改进的人工蜂群算法提升了37.5%,体现了所提混合Jaya算法在迭代精度和速度上的优越性。
To improve the calculation accuracy and convergence speed of flatness error evaluation,a flatness error evaluation algorithm based on Hybrid Jaya algorithm was proposed.Firstly,on the basis of minimum zone method,a mathematical model for flatness error is constructed;Secondly,the Opposition-Based Learning is introduced into the basic Jaya algorithm twice to enhance the quality of the solution;Thirdly,in the iterative process,the Nelder-Mead simplex algorithm is applied to improve the local search ability of the algorithm;Finally,the proposed algorithm was experimentally validated through multiple sets of examples.The results show that the hybrid Jaya algorithm has higher computational accuracy than genetic algorithm,particle swarm optimization algorithm,plant growth simulation algorithm,basic Jaya algorithm,and artificial bee colony algorithm,for iteration speed,it has faster convergence speed compared to the above algorithm,and it has increased by 37.5%compared to the improved artificial bee colony algorithm,which reflect the superiority of the proposed hybrid Jaya algorithm in terms of iteration accuracy and speed.
作者
杨洋
汪顺利
陈智超
范晓骏
YANG Yang;WANG Shunli;CHEN Zhichao;FAN Xiaojun(Shanghai Aircraft Manufacturing Company,Shanghai 021324,China)
出处
《组合机床与自动化加工技术》
北大核心
2023年第11期150-153,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
中国商飞创新基金项目(30302300022)。
关键词
平面度
最小区域法
Jaya算法
反向学习
下山单纯形法
flatness
minimum zone method
Jaya algorithm
opposition-based learning
nelder-mead simplex algorithm
