Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power...

-In order to overcome the defects of the traditional static load modeling in coalmine, a new modeling method is proposed in this paper. First, a new clustering method based on improved PSO algorithm is presented to classify the load data in order to reduce the number of load model before modeling. Second, RBF neural network based on improved PSO algorithm is put forward to establish aggregate l...

Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, because of its flexibility and dynamic nature. It is proven that the clustering technique, as a multi objective optimization, is the most effective solution to have minimum energy consumption. The goal of the clustering technique is to divide network sensors into clusters each of which has a cluster-head...

It has been proved by several researchers that particle swarm optimization has shown better results for clustering large datasets. In this paper we present a different approach to that of conventional particle swarm optimization technique. We have used aiNet algorithm of Artificial Immune System(AIS) to preprocess the data i.e. generating the antibodies with more affinity values among different...

Particle swarm optimization (PSO) is recent approach that can be employed in a wide range of applications. It is an evolutionary computing method based on colony aptitude which is a better parallel searching algorithm. Image segmentation is a low level vision task which is applicable in various applications such as object recognition, medical imaging, document analysis, just to name a few. PSO ...

This paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. During recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. In general, many quantitative and qualitative factors, such as quality, price, and fl...

One of the fundamental steps in the transformation of the LIDAR data into the meaningful objects in urban area involves their segmentation into consistent units through a clustering process. Nevertheless, due to the scene complexity and the variety of objects in urban area, e.g. buildings, roads, and trees, it is clear that a clustering using only a single cue will not suffice. Considering the ...

Traditional clustering algorithms, such as K-Means, perform with a single goal in mind. However, many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional algorithms have drawbacks centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based approaches were developed to address these shortcomings. Anim...

In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank...