摘要
针对传统启发式智能优化算法评定圆度误差计算效率低且容易陷入局部最优解的问题,提出采用改进乌鸦搜索算法评定圆度误差。根据最小区域拟合准则建立乌鸦搜索算法评定圆度误差数学模型,并引入权重系数,提高算法全局搜索能力,同时设定最小二乘圆心附近为乌鸦搜索初始位置,提高算法搜索效率。最后通过模拟和实验验证了所提算法的准确性和高效性,并通过多组数据对比发现改进乌鸦搜索算法的全局搜索能力较遗传算法(GA)、粒子群算法(PSO)和传统乌鸦搜索算法(CSA)得到明显提升。
In order to solve the problem that the conventional heuristic optimization algorithms is inefficient in evaluating roundness error and easy to fall into local optimal solution,an improved crow search algorithm was introduced to evaluate roundness error.The mathematical model for evaluating roundness errors of crow search algorithm was formulated based on the minimum zone circle(MZC) fitting criterion,and the weight coefficient was introduced to enhance the global search capability of the algorithm.At the same time,the initialization of the starting position in proximity to the center of the least squares circle was implemented to improve the algorithm′s search efficiency.Finally,the accuracy and precision of the proposed algorithm were validated through simulations and experiments.It is found that the global search ability of the improved crow search algorithm is significantly improved compared with genetic algorithm(GA),particle swarm optimization(PSO) and traditional crow search algorithm(CSA) by comparing multiple sets of data.
作者
张志永
郑鹏
王世强
郝用兴
ZHANG Zhiyong;ZHENG Peng;WANG Shiqiang;HAO Yongxing(Department of Mechanical Engineering,Zhengzhou University of Science and Technology,Zhengzhou Henan 450064,China;School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou Henan 450001,China;School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China;School of Mechanical Engineering,North China University of Water Resources and Electric Power,Zhengzhou Henan 450045,China)
出处
《机床与液压》
北大核心
2024年第19期65-70,共6页
Machine Tool & Hydraulics
基金
河南省科技厅科技攻关项目(232102220082)。
关键词
圆度误差
乌鸦搜索算法
最小二乘法
最小区域法
roundness error
crow search algorithm
least squares circle
minimum zone circle
