Jian YANG, Hong-yu ZHAO. The research of floating-point codingimproved genetic algorithmin flatnesserror evaluation[J]. Optics and precision engineering, 2017, 25(3): 706-711.
DOI:
Jian YANG, Hong-yu ZHAO. The research of floating-point codingimproved genetic algorithmin flatnesserror evaluation[J]. Optics and precision engineering, 2017, 25(3): 706-711. DOI: 10.3788/OPE.20172503.0706.
The research of floating-point codingimproved genetic algorithmin flatnesserror evaluation
With rapid development of intelligent manufacturing system
using Meta heuristic method to quickly and accurately calculate the flatness error is of great practical significance. To further improve the accuracy of flatness error calculation
an improved genetic algorithm based on floating-point coding was studied. In this method
the simulated annealing idea was introduced and a mathematic model for minimum zone method was established based on crossover and variation of the original genetic algorithm; and then the optimal fitness convergence curve and average fitness convergence curve were obtained through computer simulation. The optimization results show that compared with traditional genetic algorithm
the accuracy of flatness error calculation is improved by 33.67%. The algorithm adopts floating-point coding
three section cross
turning wheel selection and optimal preservation strategy; and its overall performance can be improved by local search advantage of the simulated annealing algorithm. Being more convenient for computer programming
the algorithm can be further applied to other high-accuracy position and dimension calculations of intelligent measuring instruments.
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references
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