TCPSO (Two-swarm Cooperative Particle Swarm Optimization) has been proposed by Sun and Li in 2014. TCPSO divides the swarms into two groups with different migration rules, and it has higher performance for high-dimensional nonlinear optimization problems than traditional PSO and other modified method of PSO. This paper proposes a particle swarm optimization by modifying TCPSO to avoid inappropr...

An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets ...

In this paper we are going to survey the application of particle swarm optimization (PSO) in WSN over different type of clustering based algorithm techniques like LEACH,LEACH-C, PEGASIS, etc In WSN sensors are randomly deployed in the sensor field which brings the coverage problem. Hence energy and coverage problem are very scarce resources for such sensor systems and has to be managed wisely i...

Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the prese...

Classification tasks often involve a large number of features, where irrelevant or redundant features may reduce the classification performance. Such tasks typically requires a feature selection process to choose a small subset of relevant features for classification. This paper proposes a new representation in particle swarm optimisation (PSO) to utilise statistical clustering information to s...

For Multi-Objective Optimal Reactive Power Dispatch (MORPD), a new approach is proposed as a simultaneous minimization of the active power transmission loss, the bus voltage deviation and the voltage stability index of a power system are obtained. Optimal settings of continuous and discrete control variables (e.g., generator voltages, tap positions of tap changing transformers and the number of...

The main objective of the paper is to find the motif information.The functionalities of the proteins are ideally found from their motif information which is extracted using various techniques like clustering with k-means, hybrid k-means, self-organising maps, etc., in the literature. In this work protein sequence information is extracted using optimised k-means algorithm. The particle swarm opt...

This paper is concerned with an optimization of GN (Granular Networks) based on PSO (Particle Swarm Optimization) and Information granulation). The GN is designed by the linguistic model using context-based fuzzy c-means clustering algorithm performing relationship between fuzzy sets defined in the input and output space. The contexts used in this paper are based on two-sided Gaussian membershi...

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SO...

In this era of fast computational machines and new optimization algorithms, there have been great advances in Experimental Designs. We focus our research on design issues in generalized linear models (GLMs) and functional magnetic resonance imaging (fMRI). The first part of our research is on tackling the challenging problem of constructing exact designs for GLMs, that are robust against parame...