基于天牛须改进粒子群算法的平面度误差评定研究

doi:10.3969/j.issn.1000-1158.2021.01.02

Abstract
Figure/Table
References (17)
Related Citation (15)

Download: PDF (1878 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract A particle swarm optimization algorithm based on the beetle antennae search algorithm (BAS-PSO) is proposed to evaluate flatness errors. Firstly, a mathematical model for evaluating the flatness error based on the minimum region is established and the objective function is transformed into a nonlinear optimization problem. Secondly, on the basic of particle swarm optimization algorithm (PSO), the beetle antennae search algorithm (BAS) with strong global search ability is introduced. As a result, the parallel computation of global search and local search is sped up to avoid premature convergence and falling into local optimization, and the accuracy and efficiency of flatness error evaluation is improved. Finally, the effectiveness of BAS-PSO is experimented by Rosenbrock and Schaffer test functions, BAS-PSO is used to solve the objective function based on the evaluation mathematical model of flatness error of the minimum region, the experimental results show that the algorithm is better than BAS and PSO. The algorithm was applied to the sample measurement of flatness error, the tolerance value of flatness is 0.00615mm, the average tolerances of BAS-PSO are reduced 0.0023mm, 0.00127mm, 0.00058mm, and 0.0037mm compering with the least square method (LSM), genetic algorithm (GA), BAS, and PSO, which verified the feasibility and superiority of the algorithm.

Key words： metrology flatness error BAS PSO algorithm error evaluation

Received: 13 April 2020 Published: 19 January 2021

PACS:

TB92

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIU Chao
	WANG Chen
	ZHONG Yu-ning

Cite this article:

LIU Chao,WANG Chen,ZHONG Yu-ning. A Particle Swarm Optimization Algorithm Based on Beetle Antennae Search for Flatness Error Evaluation[J]. Acta Metrologica Sinica, 2021, 42(1): 9-15.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.01.02 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I1/9

［1］刘永超, 陈明. 形位误差的进化算法［J］. 计量学报, 2001, 22(1): 18-22.
Liu Y C, Chen M. An evolutionary algorithm for shape and position errors ［J］. Acta Metrologica Sinica, 2001, 22(1): 18-22.
［2］黄富贵, 崔长彩. 评定直线度误差的最小二乘法与最小包容区域法精度之比较［J］. 光学精密工程, 2007, 15(6): 889-893.
Huang F G, Cui C C. Comparison of the accuracy of the least square method and the method of the least inclusion region ［J］. Optical precision engineering, 2007, 15(6): 889-893.
［3］费兰, 杜世昌. 面向零件平面度误差估计的空间泛克里金插值建模［J］. 机械工程学报, 2014, 50(15): 127-135.
Fei L, Du S C. Spatial pan-kriging interpolation modeling for part planeness error estimation ［J］. Journal of mechanical engineering, 2014, 50(15): 127-135.
［4］康岩辉, 王冰鹤, 崔京远, 等. 基于多角度旋转的高精度平晶测量［J］. 计量学报, 2017, 38(z1): 25-28.
Kang Y H, Wang B H, Cui J Y, et al. High precision flat crystal measurement based on multi Angle rotation ［J］. Acta Metrologica Sinica, 2017, 38(z1): 25-28.
［5］Chakraborti N, Siva Kumar B, Satish Babu V, et al. Optimizing surface profiles during hot rolling: A genetic algorithms based multi-objective optimization ［J］. Computational Materials Science, 2005, 37(1): 30-34.
［6］温秀兰, 宋爱国. 基于实数编码的改进遗传算法及在平面度误差评定中的应用［J］. 计量学报, 2003, 24(2): 88-91.
Wen X L, Song A G. An improved genetic algorithm based on real number coding and its application in flatness error evaluation ［J］. Acta Metrologica Sinica, 2003, 24(2): 88-91.
［7］罗钧, 王强, 付丽. 改进蜂群算法在平面度误差评定中的应用［J］. 光学精密工程, 2012, 20(2): 422-430.
Luo J, Wang Q, Fu L. Application of improved swarm algorithm in flatness error assessment ［J］. Optical precision engineering, 2012, 20(2): 422-430.
［8］刘浩然, 崔静闯, 卢泽丹, 等. 一种改进的雁群扩展粒子群算法研究［J］. 计量学报, 2019, 40(3): 498-504.
Liu H R, Cui J C, Lu Z D, et al. An improved extended particle swarm optimization algorithm based on goose swarm algorithm ［J］. Acta Metrologica Sinica, 2019, 40(3): 498-504.
［9］毕立恒, 朱彦齐. 基于分群粒子群算法的平面度误差评定研究［J］. 计量学报, 2019, 40(6): 980-985.
Bi L, H, Zhu Y, Q. Evaluation of flatness error based on particle swarm optimization ［J］. Acta Metrologica Sinica, 2019, 40(6): 980-985.
［10］王傲胜. 基于测量不确定度的平面度误差搜索范围研究［J］. 计量学报, 2017, 38(2): 168-170.
Wang A S. Based on the uncertainty of measurement of planeness error search scope of research ［J］. Acta Metrologica Sinica, 2017, 38(2): 168-170.
［11］Kanada T, Suzuki S. Evaluation of minimum zone flatness by means of nonlinear optimization techniques and its verification ［J］. Precision Engineering, 1993, 15(2): 93-99.
［12］Jiang X Y, Li S. Beetle antennae search algorithm for optimization problems ［J］. Mathematical Problems in Engineering, 2017, 24(10): 1712-1716.
［13］Wu Q, Lin H, Jin Y Z, et al. A new fallback beetle antenna search algorithm for path planning of mobile robot with collision-free capability ［J］. Soft Computing, 2020, 24: 2369-2380.
［14］Pablo G T, Francisco L I, Carlos A. Long term optimization based on PSO of a grid-connected renewable energy hybrid system ［J］. International Journal of Hydrogen Energy, 2014, 39(21): 10805-10816.
［15］Yu X W, Zhan G C, Hong F Z, et al. An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint ［J］. Applied Sciences, 2020, 10(5): 332-338.
［16］王跃灵, 旺玥, 王琪, 等. 基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识［J］. 计量学报, 2020, 41(1): 60-66.
Wang Y L, Wang Y, Wang Q, et al. Dynamic parameter identification of flexible joint robot based on adaptive particle swarm optimization ［J］. Acta Metrologica Sinica, 2020, 41(1): 60-66.
［17］Jorge I P, Bruno M, Niels E C, et al. Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind-Wave Farm ［J］. Processes, 2020, 8(2): 78-82.