Swarm

to enhance the quality of medical image. But still there is scope to enhance the image. In the proposed method, finding out the seed pixel randomly is basic issue, is treated as an optimization problem, it can be solved by Particle swarm optimization. Using particle swarm optimization algorithm , the fitness function can give us appropriate seed pixel for required ultrasound imaging .In this paper, a novel method wherein segmentation will be applied on the filtered image i.e fuzzy filter. Fuzzy filter

Feeder Reconfiguration is basically an optimization problem which has several objectives and operating constraints. A. Merlin and H. Black were the first to implement optimization techniques to solve a Feeder Reconfiguration problem. Optimization is a mathematical tool to obtain an optimized solution. There are numerous techniques to solve an optimization problem where in each technique will yield a different network topology and different optimized solution. Heuristic and meta-Heuristic methods

GAVNS: Guided Ant Colony Optimization based Variable Neighborhood Search for Optimistic Load Balancing in Grid Computing Gurveer Kaur Brar1, Amit Chhabra2 1 Guru Nanak Dev University, Amritsar Punjab, India gurveer.dhillon43@gmail.com, amit.cse@gndu.ac.in Abstract Grid Computing resolves high performance computing and throughput issues through sharing of resources. These resources are heterogeneous in nature and geographically distributed to develop large scale applications. Scheduling

we propose a novel heuristic search technique to solve this problem. Our approach is a multi-population search algorithm based on the Particle Swarm Optimization (PSO). The goal of this algorithm is to search for sensor network layouts that maximize both the coverage and lifetime of the network. Unlike traditional PSO, our algorithm assignes a swarm to each sensor in the network and a global network topology is used to evaluate the

the depth information details. Particle Swarm Optimization and K-means algorithms are used for image segmentation. Our main objective is to implement stereo matching algorithms on the segmented images, compare the results of K-means and PSO on the basis of objective parameters such as PSNR, execution time, density of disparity map and compression ratio and perform subjective analysis of reconstructed 3-D images. The compared results show that the Particle Swarm Optimization algorithm gives better 3-D

quality of medical image. However, there is scope to further enhance the image. In the proposed method, finding out the seed pixel randomly is the basic problem, which is treated as an optimization problem. It can be solved by Particle Swarm Optimization. Using Particle Swarm Optimization algorithm, the fitness function can give us appropriate seed pixel for required ultrasound imaging. In this paper, a novel method is proposed, wherein segmentation will be applied on a fuzzy filtered image. The fuzzy

increasingly more difficult to maintain synchronism between various parts of the power system. This paper work presents an advanced adaptive Particle swarm optimization technique to optimize the SVC controller parameters for enhancement of the steady state stability & overcoming the premature convergence & stagnation problems as in basic PSO algorithm & Particle swarm optimization with shrinkage factor & inertia weight approach (PSO-SFIWA). In this paper SMIB system along with PID damped SVC controller is

Voltage instability problems play great role in power systems planning and operation. Nowadays, power systems are being performed closer to their steadiness limits due to economic and environmental constrains. Maintaining a fixed and secure operation of power system is hence a very vital and challenging issue. Therefore, it is highly suggested to take care of voltage stability especially in high enhanced networks as a consequence of heavier loading. Many researchers study the power system voltage

Engineering A.U. College of Engineering (A) Visakhapatnam, India vaisakh_k@yahoo.co.in Abstract— In this paper, determination of optimal parameters of a PID controller in an Automatic Voltage Regulation (AVR) system by the approach of Particle Swarm Optimization (PSO) and Differential Evolution (DE) Techniques is presented. This paper demonstrated in detail how to employ the PSO and DE methods to search efficiently the optimal PID controller parameters of an AVR system. A MATLAB simulation has

processing, the MapReduce design concept is quite suitable for use with particle swarm algorithm with parallel computing concept combined. Therefore, this study will use Hadoop platform, raised MRuPSO algorithm, it was integrated PSO (Particle Swarm Optimization) and MapReduce architecture. In this study, we focused on the typical Computational Intelligence algorithm: Particle Swarm Optimization (PSO). PSO is a useful utility swarm intelligence