Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. The term “clustering” is used in several research communities to describe methods for grouping of unlabeled data. These communities have different terminologies and assumptions for the components of the clustering process and the context in which clustering is used. This paper looks into th...

This paper describes a method for the estimation of a dynamic open contour by incorporating a modified particle swarm optimization technique. This scheme has been applied to a “Particle Image Velocimetry” experiment for the analysis of fluid turbulence during a hydraulic jump. Due to inter reflections within the medium and refractions across different media interfaces, the imagery contains spur...

This paper describes an implementation of automatic mutual localization of swarm robots using a particle filter. Each robot determines the location of the other robots using wireless sensors. The measured data will be used for determination of the movement method of the robot itself. It also affects the other robots’ self-arrangement into formations such as circles and lines. We discuss the pro...

While swarms that execute decisions are well known in the swarm community, swarms that exhibit this capability a priori have never before been achieved. We demonstrate a methodology, based on the Hamiltonian method of swarm design, that enables the design and implementation of swarms that exhibit decision-making capability. We develop the theoretical structure of the method and apply it to the ...

This paper proposes an optimal congestion management approach under hybrid electricity market using Self organizing hierarchical particle swarm optimization with Time Varying Acceleration Coefficients (SPSO-TVAC). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost under hybrid electricity market. The values of Transmission C...

Diversity control in the particle swarm optimization (PSO) algorithm is one of the important issues that influence the process of finding global optimal solution. In this study we create a historical process to find best area of the search space for population dispersion guide on PSO algorithm, and name Diversity Guided Particle Swarm Optimization algorithm (DGPSO) algorithm. Hence we propose a...

Efficient network coverage and connectivity are the requisites for most Wireless Sensor Network deployments, particularly those concerned with area monitoring. The Coverage Control Technology is one of the basic technologies of wireless sensor network, and is mainly concerned about how to prolong the network lifetime on the basis of meeting users’ perception demand. To optimize wireless sensor ...

The key behind the research represent in this paper is to understand the behavior of the particle swarm algorithm. This study proposes guaranteed convergence Particle Swarm Optimizer (GCPSO) with various topologies. The proposed GCPSO has evaluated the topology such as GBest, LBest, and Von Neumann Topology. It would be the most appropriate for different benchmark function such as Quadratic, Ro...

Genetic algorithm (GA) is a kind of method to simulate the natural evolvement process to search the optimal solution, and the algorithm can be evolved by four operations including coding, selecting, crossing and variation. The particle swarm optimization (PSO) is a kind of optimization tool based on iteration, and the particle has not only global searching ability, but also memory ability, and ...

In this paper, we intend to study the synthesis of the multibeam arrays. The synthesis implementation’s method for this type of arrays permits to approach the appropriated radiance’s diagram. An adaptive particle swarm optimization algorithm (APSO) is proposed to synthesis multibeam antenna arrays. The problem is formulated and solved by means of the proposed algorithm. The examples are simulat...