2013•Unpublished venueRequires access

The global double cubic B-spline surface interpolation based on particle swarm optimization

Tiansong Li, Xinling Shi, Jianhua Chen, Yajie Liu, Baolei Li, Changxing Gou

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Abstract

a novel B-spline surface interpolation algorithm based on particle swarm optimization is proposed to solve surface optimization problem. Two steps of the algorithm are established in present paper: first, control points are calculated by a given cloud of 3D data points. Second, a set of optimal parameters of the data points is obtained by using particle swarm optimization. The results are compared with the sample points to minimize the root square error. Compared to the traditional method with the smallest root square error, the experimental results have shown that particle swarm optimization yields better solution.

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What this paper is about

a novel B-spline surface interpolation algorithm based on particle swarm optimization is proposed to solve surface optimization problem. Two steps of the algorithm are established in present paper: first, control points are calculated by a given cloud of 3D data points. Second, a set of optimal parameters of the data points is obtained by using particle swarm optimization. The results are compared with the sample points to minimize the root square error. Compared to the traditional method with the smallest root square error, the experimental results have shown that particle swarm optimization yields better solution.

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Available abstract

a novel B-spline surface interpolation algorithm based on particle swarm optimization is proposed to solve surface optimization problem. Two steps of the algorithm are established in present paper: first, control points are calculated by a given cloud of 3D data points. Second, a set of optimal parameters of the data points is obtained by using particle swarm optimization. The results are compared with the sample points to minimize the root square error. Compared to the traditional method with the smallest root square error, the experimental results have shown that particle swarm optimization yields better solution.

Key concepts: Particle swarm optimization, Multi-swarm optimization, Interpolation (computer graphics), Mathematical optimization, Metaheuristic, Spline interpolation, Algorithm, Surface (topology)

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