Fitting scattered data points with ball B-Spline curves using particle swarm optimization

2018 ◽  
Vol 72 ◽  
pp. 1-11 ◽  
Author(s):  
Zhongke Wu ◽  
Xingce Wang ◽  
Yan Fu ◽  
Junchen Shen ◽  
Qianqian Jiang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Scattered Data ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curves ◽  
Data Points

Airfoil Topology Optimization using Teaching-Learning based Optimization

2015 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Dushhyanth Rajaram ◽  
Himanshu Akhria ◽  
S. N. Omkar
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Spline Curves ◽  
Conceptual Design Phase ◽  
Design Variables ◽  
Heuristic Technique ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper primarily deals with the optimization of airfoil topology using teaching-learning based optimization, a recently proposed heuristic technique, investigating performance in comparison to Genetic Algorithm and Particle Swarm Optimization. Airfoil parametrization and co-ordinate manipulations are accomplished using piecewise b-spline curves using thickness and camber for constraining the design space. The aimed objective of the exercise was easy computation, and incorporation of the scheme into the conceptual design phase of a low-reynolds number UAV for the SAE Aerodesign Competition. The 2D aerodynamic analyses and optimization routine are accomplished using the Xfoil code and MATLAB respectively. The effects of changing the number of design variables is presented. Also, the investigation shows better performance in the case of Teaching-Learning based optimization and Particle swarm optimization in comparison to Genetic Algorithm.

B-Spline Curve Fitting Based on Adaptive Particle Swarm Optimization Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1299-1304 ◽  
Author(s):  
Yue Hong Sun ◽  
Zhao Ling Tao ◽  
Jian Xiang Wei ◽  
De Shen Xia
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Control Points ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curve

For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.

scholarly journals Nonlinear Time Series Prediction Model Based on Particle Swarm Optimization B-spline Network

2018 ◽  
Vol 51 (21) ◽  
pp. 219-223
Author(s):  
Lingshuang Kong ◽  
Xiaolong Gong ◽  
Chuanlai Yuan ◽  
Huiqin Xiao ◽  
Jianhua Liu
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Nonlinear Time Series ◽  
Swarm Optimization ◽  
B Spline ◽  
Model Based

Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization

2019 ◽  
Vol 23 (3) ◽  
pp. 592-601 ◽  
Author(s):  
Hung Quoc Truong ◽  
◽  
Long Thanh Ngo ◽  
Long The Pham
Keyword(s):  
Particle Swarm Optimization ◽  
Processing Speed ◽  
Particle Swarm ◽  
Improve Performance ◽  
Swarm Optimization ◽  
Local Optima ◽  
Gravitational Forces ◽  
Data Points ◽  
Interval Type

The interval type-2 fuzzy possibilistic C-means clustering (IT2FPCM) algorithm improves the performance of the fuzzy possibilistic C-means clustering (FPCM) algorithm by addressing high degrees of noise and uncertainty. However, the IT2FPCM algorithm continues to face drawbacks including sensitivity to cluster centroid initialization, slow processing speed, and the possibility of being easily trapped in local optima. To overcome these drawbacks and better address noise and uncertainty, we propose an IT2FPCM method based on granular gravitational forces and particle swarm optimization (PSO). This method is based on the idea of gravitational forces grouping the data points into granules and then processing clusters on a granular space using a hybrid algorithm of the IT2FPCM and PSO algorithms. The proposed method also determines the initial centroids by merging granules until the number of granules is equal to the number of clusters. By reducing the elements in the granular space, the proposed algorithms also significantly improve performance when clustering large datasets. Experimental results are reported on different datasets compared with other approaches to demonstrate the advantages of the proposed method.

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