The routing protocol of WSN based on clustering hierarchy has been one of the important research topics. An improved strategy based on LEACH (Low Energy Adaptive Clustering Hierarchy) is proposed in this paper, which uses the PSO algorithm to optimize clustering process by considering the energy, the communication costs, the load balance and other factors to determine the cluster-head node. The...

This study proposes a novel Intuitionistic fuzzy c-least squares support vector regression (IFCLSSVR) with sammon mapping clustering algorithm. The proposed clustering algorithm can obtain the advantages of intuitionistic fuzzy sets, LSSVR, and sammon mapping in actual clustering problems. Moreover, IFC-LSSVR with sammon mapping adopts particle swarm optimization (PSO) to search optimal paramet...

with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

This paper presents a new approach for power signal time series data mining using S-transform based K-means clustering technique. Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the K-means algorithm for disturbance event detection....

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique acc...

Particle swarm optimization (PSO) is a popular bio-inspired algorithm which is applied to solve various optimization problems in many areas including machine intelligence, data mining, robotics and computer networks. In this paper, we propose a PSO-based scheme to solve hot spot problem caused by multi-hop communication in a cluster-based wireless sensor network. The scheme consists of routing ...

An attempt has been made in this paper to solve the non-linear and ill-posed Electrical Impedance Tomography (EIT) inverse problem using clustering-based particle swam optimization (PSO). To enhance optimal search capability in such an ultra high dimension space and improve the quality of the reconstructed image, an adaptive PSO algorithm combined with a modified Newton–Raphson algorithm and a ...

Wireless Sensor Network (WSN) is a self-organizing network which formed with huge sensors which are located in an application specific environment to monitor the physical pheromone like temperature, fire, and pressure. Mostly sensors are equipped with battery power through which they can perform sufficient operations and communication among neighboring nodes. Researchers for WSNs face challenge...

The authors present a combined method for order reduction of higher order linear dynamic systems using the advantages of the improved pole clustering and error minimization by PSO. The denominator of the reduced model is obtained by the improved pole clustering and PSO is employed for determining numerator coefficients by minimizing the integral square error between the transient responses of t...

It is well known that solutions of k-means algorithm depend on the initialization of cluster centers and the final solution converges to local minima. In this paper, we introduce a clustering approach that combines ideas from modified particle swarm optimization (PSO) and k-means. The potential benefits of this technique are investigated by incorporating the multidimensional asynchronism and st...