An ant colony approach for clustering

GENERALIZED PARTICLE MODEL USED FOR DATA CLUSTERING

Data clustering has been widely used in many areas, such as data mining, statistics, machine learning and so on. A variety of clustering approaches have been proposed so far, but most of them are not qualified to quickly cluster a large-scale high-dimensional database. This paper is devoted to a novel data clustering approach based on a generalized particle model (GPM). The GPM transforms the data clustering process into a stochastic process over the configuration space on a GPM array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, openness and easier hardware implementation with the VLSI systolic technology. The analysis and simulations have shown the effectiveness and good performance of the proposed GPM approach to data clustering.

A powerful and efficient evolutionary optimization algorithm based on stem cells algorithm for data clustering

There are many ways to divide datasets into some clusters. One of most popular data clustering algorithms is K-means algorithm which uses the distance criteria for measuring the data correlation. To do that, we should know in advance the number of classes (K) and choose K data points as an initial set to run the algorithm. However, the choice of initial points is a main problem in this algorithm which may cause the algorithm to converge to a local minimum. Some other data clustering algorithms have been proposed to overcome this problem. The methods are Genetic algorithm (GA), Ant Colony Optimization (ACO), PSO algorithm, and ABC algorithms. In this paper, we employ the Stem Cells Optimization algorithm for data clustering. The algorithm was inspired by behavior of natural stem cells in the human body. We developed a new data clustering based on this new optimization scheme which has the advantages such as high convergence rate and easy implementation process. It also avoids local minimums in an intelligent manner. The experimental results obtained by using the new algorithm on different well-known test datasets compared with those obtained using other mentioned methods demonstrate the better accuracy and high speed of the new algorithm.

Parameter estimation of dynamical systems via a chaotic ant swarm

Through the construction of suitable objective function, the parameter estimation of the dynamical system could be converted to the problem of parameter optimization. Based on the chaotic ant swarm optimization approach, we investigate the problem of parameter optimization for the dynamical systems in the presence of noise. We systematically analyze the basic relationships among the complexity of objective function, the length of time series, and the performance of the searching algorithm. Furthermore, we consider the effect of measurable additive noise on the objective function. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed methods.

Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data

This paper introduces the ant colony algorithm, a novel swarm intelligence based optimization method, to select appropriate wavelet coefficients from mass spectral data as a new feature selection method for ovarian cancer diagnostics. By determining the proper parameters for the ant colony algorithm (ACA) based searching algorithm, we perform the feature searching process for 100 times with the number of selected features fixed at 5. The results of this study show: (1) the classification accuracy based on the five selected wavelet coefficients can reach up to 100% for all the training, validating and independent testing sets; (2) the eight most popular selected wavelet coefficients of the 100 runs can provide 100% accuracy for the training set, 100% accuracy for the validating set, and 98.8% accuracy for the independent testing set, which suggests the robustness and accuracy of the proposed feature selection method; and (3) the mass spectral data corresponding to the eight popular wavelet coefficients can be located by reverse wavelet transformation and these located mass spectral data still maintain high classification accuracies (100% for the training set, 97.6% for the validating set, and 98.8% for the testing set) and also provide sufficient physical and medical meaning for future ovarian cancer mechanism studies. Furthermore, the corresponding mass spectral data (potential biomarkers) are in good agreement with other studies which have used the same sample set. Together these results suggest this feature extraction strategy will benefit the development of intelligent and real-time spectroscopy instrumentation based diagnosis and monitoring systems.

An ant algorithm for the conformational analysis of flexible molecules

Originally, the ant system was developed for optimization in discrete search spaces such as the traveling salesman problem. We detail our adaptation of the algorithm to optimization in the continuous search space of conformational analysis. The parameters of the algorithm were tuned using a simple test molecule, undecane, and a drug molecule, imatinib. The algorithm is further tested on four more drug or drug-like molecules, on vitamin A and on alanine tetrapeptide.