Abstract—In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension apriori,which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better “guide ” than the PSO’s native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem. Index Terms—Fractional global best formation (FGBF), multidimensional (MD) search, particle swarm optimization (PSO). I.

Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data “on the run. ” Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful well-known optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting real-world application of the proposed method to automatic segmentation of images is also reported.

Abstract — This article introduces a scheme for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring groups in the data. The proposed method is based on a modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the Multi-elitist PSO (MEPSO) model. It also employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed high-dimensional feature space. A new particle representation scheme has been adopted for selecting the optimal number of clusters from several possible choices. The performance of the proposed method has been extensively compared with a few state of the art clustering techniques over a test suit of several artificial and real life datasets. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of the PSO algorithm.

This paper introduces a method for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring clusters. The proposed method is based on an improved variant of the Particle Swarm Optimization (PSO) algorithm. In addition, it employs a kernelinduced similarity measure instead of the conventional sum-ofsquares distance. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed highdimensional feature space. Computer simulations have been undertaken with a test bench of five synthetic and three real life datasets, in order to compare the performance of the proposed method with a few state-of-the-art clustering algorithms. The results reflect the superiority of the proposed algorithm in terms of accuracy, convergence speed and robustness. Categories and Subject Descriptors

Abstract — Image segmentation based on an adaptive K-means clustering algorithm is presented. The proposed method tries to develop K-means algorithm to obtain high performance and efficiency. This method proposes initialization step in K-means algorithm. In addition, it solves a model selection number by determining the number of clusters using datasets from images by frame size and the absolute value between the means, and additional steps for convergence step in K-means algorithm are added. Moreover, in order to evaluate the performance of the proposed method, the results of the proposed method, standard K-means and recently modified K-means are compared. The experimental results showed that the proposed method provides better output.

In this paper, well-known PSO algorithms reported in the literature for solving continuous function optimization problems were comparatively evaluated by considering real world data clustering problems. Data clustering problems are solved, by considering three performance clustering metrics such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the PSO variants were compared with the basic PSO algorithm, Genetic algorithm and Differential evolution algorithms. A detailed performance analysis has been carried out to study the convergence behavior of the PSO algorithms using run length distribution. Key words:

A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise. Key words: Comprehensive learning particle swarm optimization, Fuzzy

Abstract—Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utter importance since human visual system primarily uses them for perception. In this paper we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multi-Dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. To address this problem we then present Fractional Global Best Formation (FGBF) technique, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better “guide ” than the PSO’s native gbest particle. We finally propose an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique. Keywords-dominant color, particle swarm optimization, dynamic clustering I.

Abstract — Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multi-Dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then present Fractional Global Best Formation (FGBF) technique, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better “guide ” than the PSO’s native gbest particle. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique. Index Terms—edge detection, multi-scale analysis, multimedia indexing and retrieval, shape descriptor over subsegments. D I.

Abstract- This paper describes an evolutionary clustering algorithm, which can partition a given dataset automatically into the optimal number of groups through one shot of optimization. The proposed method is based on an evolutionary computing technique known as the Bacterial Evolutionary Algorithm (BEA). The BEA draws inspiration from a biological phenomenon of microbial evolution. Unlike the conventional mutation, crossover and selection operaions in a GA (Genetic Algorithm), BEA incorporates two special operations for evolving its population, namely the bacterial mutation and the gene transfer operation. In the present context, these operations have been modified so as to handle the variable lengths of the chromosomes that encode different cluster groupings. Experiments were done with several synthetic as well as real life data sets including a remote sensing satellite image data. The results estabish the superiority of the proposed approach in terms of final accuracy.