A Smooth Clustering Algorithm Based on Parameter Free Filled Function

2010 ◽  
Vol 143-144 ◽  
pp. 389-393
Author(s):  
Qing Wu ◽  
Li Xing Yuan
Keyword(s):  
Optimization Model ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Filled Function ◽  
Filled Function Method ◽  
Global Optimal Solution ◽  
Entropy Factor ◽  
Global Optimal ◽  
Clustering Center ◽  
Entropy Technique

In this paper, we propose an algorithm to find centers of clusters based on adjustable entropy technique. A completely differentiable non-convex optimization model for the clustering center problem is constructed. A parameter free filled function method is adopted to search for a global optimal solution of the optimization model. The proposed algorithm can avoid the numerical overflow phenomenon. Numerical results illustrate that the proposed algorithm can effectively hunt centers of clusters and especially improve the accuracy of the clustering even with a relatively small entropy factor.

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

Fuzzy Clustering Segmentation Algorithm Research on Sports Image

2013 ◽  
Vol 339 ◽  
pp. 297-300 ◽  
Author(s):  
Xue Jiao Dong ◽  
Xiao Yan Zhang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Practical Method ◽  
Global Optimal Solution ◽  
Fcm Algorithm ◽  
Fast Convergence Rate ◽  
Fuzzy C Means Clustering ◽  
Global Optimal ◽  
Theory Research

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM is proposed. Firstly, the approximate optimal solution obtained by the improved FCM algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution, the final segmentation result is achieved at last. The experiment results prove that in the view of the flame image segmentation, this method shows the good clustering performance and fast convergence rate, and has the widespread serviceability, so it is the practical method in image segmentation.

A Gravitational Fuzzy C-Means Clustering Algorithm Based on Density Weight

2012 ◽  
Vol 182-183 ◽  
pp. 1681-1685
Author(s):  
Tian Wu Zhang ◽  
Gong Bing Guo
Keyword(s):  
Local Minimum ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Noise Data ◽  
Global Optimal ◽  
Minimum Points ◽  
Better Than

Fuzzy C-means clustering algorithm(FCM) is sensitive to its initialization of value and noise data and easy to fall into local minimum points, while it can’t get the global optimal solution. This paper introduces gravitation and density weight into the process of clustering, and proposes a gravitational Fuzzy C-Means clustering algorithm based on density weight (DWGFCM). The experimental results show that the algorithm has better global optimal solution, overcomes the shortcomings of traditional Fuzzy C-means clustering algorithm. Clustering results are obviously better than FCM algorithm.

Yarn Selection Based on Simulated Annealing Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1548-1551
Author(s):  
Li Jiang ◽  
Gang Feng Yan ◽  
Zhen Fan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Original Image ◽  
Separation Algorithm ◽  
Global Optimal Solution ◽  
Simulated Annealing Genetic Algorithm ◽  
Color Separation ◽  
Global Optimal

Aiming at the bad performance when achieve rich colors of fabric with very limited yarns in the traditional woven industry, the paper comes up with a solution of selecting yarn from a set of yarns based on SAGA(simulated annealing genetic algorithm). In order to reduce the computational complexity, original image is compressed based on clustering algorithm. And the original yarns is divided into four regions based on color separation algorithm to narrow the feasible area. The result of experiments show that image compression and yarns division can greatly improve the speed of SAGA, and SAGA can effectively converges to global optimal solution.

Optimal selection of precision points for function synthesis of spherical 4R linkage

2009 ◽  
Vol 223 (9) ◽  
pp. 2183-2189 ◽  
Author(s):  
Y Suixian ◽  
Y Hong ◽  
G Y Tian
Keyword(s):  
Optimization Model ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Scaling Factor ◽  
Optimal Selection ◽  
Global Optimal Solution ◽  
New Approach ◽  
Numerical Interpolation ◽  
Global Optimal ◽  
Selection Of

In order to reduce synthesis error, a new approach is reported to select precision points for function synthesis of spherical 4R linkages. Based on a closed-form symbolic solution of the set of function synthesis equations of spherical 4R linkages, the structure error equation has been derived by introducing a scaling factor. After this, an optimization model was constructed to search for the precision points. The initial solution of the optimization model was chosen using a numerical interpolation method. The global optimal solution was obtained using the fractal algorithm. To illustrate this, an example is presented and discussed before finally deriving conclusions.

Fuzzy Optimal Design of Tower Crane Jib

2011 ◽  
Vol 199-200 ◽  
pp. 530-533
Author(s):  
Xin Hua Li ◽  
Jian Zhou ◽  
Yi Zhang ◽  
Ling Dai
Keyword(s):  
Optimal Design ◽  
Optimization Model ◽  
Unique Feature ◽  
Optimal Solution ◽  
Amplification Coefficient ◽  
Constraint Condition ◽  
Global Optimal Solution ◽  
Tower Crane ◽  
Fuzzy Constraint ◽  
Global Optimal

This article takes the tower crane jib as the optimized object, in view of the jib unique feature, and has established the jib quality optimization objective function, then uses amplification coefficient to obtain its membership function to the fuzzy constraint condition,thus has transformed constraint condition very well. Direct searching tool-box of MATLAB software is adopted to get the optimization model,not only is simplified the optimization process,but also global optimal solution is found reliably.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

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